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DIGITAL COLLEAGUES FOR SMART AGING
| 1
Patrice D Tremoulet PhD
Director Applied Informatics Group (AIG)
bull What is a Digital Colleague (DC)
ndash Enablers
ndash Human Augmentation history (abridged)
bull 1st Use Case for DCs Aging Workforce
ndash Motivation
ndash Scope of initial effort (amp parallel projects)
ndash Approach amp Team
bull Questions Feedback
| 2
Overview
Digital Colleague (DC) Vision
| 33252015
A Digital Colleague augments human performance by serving as
1 an expert cog
2 a personal work assistant
3 a personal health coach
Like coworkers who can ldquoreadrdquo each other DCs discretely monitor
employees and provide customized assistance tailored to the
current context including the work environment physical and
cognitive states and ongoing task demands
DCs alert employees to relevant new research news or products
suggest tools and strategies that can enable better performance
and provide guidance to maintain and improve health
DC Enablers
| 43252015
1 A growing community is tackling the challenges
associated with building cognitive assistants
bull Identify and share domain knowledge customized based
upon understanding a particular employeersquos interestsneeds
2 Inexpensive wearable and environmental sensors
make it possible to reliably assess human physical and
cognitive states
bull Human Performance Augmentation
bull Cognitive supports for people with disabilities
bull Personalized health support amp health education
Human Augmentation History
1945 1960rsquos2013 +
beyond2002-2006
Vannevar Bush
As We May Think describes ldquoenlarged
intimate supplement
to onersquos memoryrdquo
(memex)
JCR Licklider
Man-Computer
Symbiosis predicts ldquohuman
brains and
computing machines
will be coupled
together very tightlyrdquo
Doug Englebart
ldquoAugmenting
Human Intellectrdquo calls for ldquoimproving the
intellectual
effectiveness of the
individual human
beingrdquo with computers
SRI ARC established
DARPA
Augmented
Cognition
Human
Performance
Augmentation in a wide variety
of domains
1990s
BRAIN
initiative
ldquodecade of
the brainrdquo
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
bull What is a Digital Colleague (DC)
ndash Enablers
ndash Human Augmentation history (abridged)
bull 1st Use Case for DCs Aging Workforce
ndash Motivation
ndash Scope of initial effort (amp parallel projects)
ndash Approach amp Team
bull Questions Feedback
| 2
Overview
Digital Colleague (DC) Vision
| 33252015
A Digital Colleague augments human performance by serving as
1 an expert cog
2 a personal work assistant
3 a personal health coach
Like coworkers who can ldquoreadrdquo each other DCs discretely monitor
employees and provide customized assistance tailored to the
current context including the work environment physical and
cognitive states and ongoing task demands
DCs alert employees to relevant new research news or products
suggest tools and strategies that can enable better performance
and provide guidance to maintain and improve health
DC Enablers
| 43252015
1 A growing community is tackling the challenges
associated with building cognitive assistants
bull Identify and share domain knowledge customized based
upon understanding a particular employeersquos interestsneeds
2 Inexpensive wearable and environmental sensors
make it possible to reliably assess human physical and
cognitive states
bull Human Performance Augmentation
bull Cognitive supports for people with disabilities
bull Personalized health support amp health education
Human Augmentation History
1945 1960rsquos2013 +
beyond2002-2006
Vannevar Bush
As We May Think describes ldquoenlarged
intimate supplement
to onersquos memoryrdquo
(memex)
JCR Licklider
Man-Computer
Symbiosis predicts ldquohuman
brains and
computing machines
will be coupled
together very tightlyrdquo
Doug Englebart
ldquoAugmenting
Human Intellectrdquo calls for ldquoimproving the
intellectual
effectiveness of the
individual human
beingrdquo with computers
SRI ARC established
DARPA
Augmented
Cognition
Human
Performance
Augmentation in a wide variety
of domains
1990s
BRAIN
initiative
ldquodecade of
the brainrdquo
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Digital Colleague (DC) Vision
| 33252015
A Digital Colleague augments human performance by serving as
1 an expert cog
2 a personal work assistant
3 a personal health coach
Like coworkers who can ldquoreadrdquo each other DCs discretely monitor
employees and provide customized assistance tailored to the
current context including the work environment physical and
cognitive states and ongoing task demands
DCs alert employees to relevant new research news or products
suggest tools and strategies that can enable better performance
and provide guidance to maintain and improve health
DC Enablers
| 43252015
1 A growing community is tackling the challenges
associated with building cognitive assistants
bull Identify and share domain knowledge customized based
upon understanding a particular employeersquos interestsneeds
2 Inexpensive wearable and environmental sensors
make it possible to reliably assess human physical and
cognitive states
bull Human Performance Augmentation
bull Cognitive supports for people with disabilities
bull Personalized health support amp health education
Human Augmentation History
1945 1960rsquos2013 +
beyond2002-2006
Vannevar Bush
As We May Think describes ldquoenlarged
intimate supplement
to onersquos memoryrdquo
(memex)
JCR Licklider
Man-Computer
Symbiosis predicts ldquohuman
brains and
computing machines
will be coupled
together very tightlyrdquo
Doug Englebart
ldquoAugmenting
Human Intellectrdquo calls for ldquoimproving the
intellectual
effectiveness of the
individual human
beingrdquo with computers
SRI ARC established
DARPA
Augmented
Cognition
Human
Performance
Augmentation in a wide variety
of domains
1990s
BRAIN
initiative
ldquodecade of
the brainrdquo
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
DC Enablers
| 43252015
1 A growing community is tackling the challenges
associated with building cognitive assistants
bull Identify and share domain knowledge customized based
upon understanding a particular employeersquos interestsneeds
2 Inexpensive wearable and environmental sensors
make it possible to reliably assess human physical and
cognitive states
bull Human Performance Augmentation
bull Cognitive supports for people with disabilities
bull Personalized health support amp health education
Human Augmentation History
1945 1960rsquos2013 +
beyond2002-2006
Vannevar Bush
As We May Think describes ldquoenlarged
intimate supplement
to onersquos memoryrdquo
(memex)
JCR Licklider
Man-Computer
Symbiosis predicts ldquohuman
brains and
computing machines
will be coupled
together very tightlyrdquo
Doug Englebart
ldquoAugmenting
Human Intellectrdquo calls for ldquoimproving the
intellectual
effectiveness of the
individual human
beingrdquo with computers
SRI ARC established
DARPA
Augmented
Cognition
Human
Performance
Augmentation in a wide variety
of domains
1990s
BRAIN
initiative
ldquodecade of
the brainrdquo
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Human Augmentation History
1945 1960rsquos2013 +
beyond2002-2006
Vannevar Bush
As We May Think describes ldquoenlarged
intimate supplement
to onersquos memoryrdquo
(memex)
JCR Licklider
Man-Computer
Symbiosis predicts ldquohuman
brains and
computing machines
will be coupled
together very tightlyrdquo
Doug Englebart
ldquoAugmenting
Human Intellectrdquo calls for ldquoimproving the
intellectual
effectiveness of the
individual human
beingrdquo with computers
SRI ARC established
DARPA
Augmented
Cognition
Human
Performance
Augmentation in a wide variety
of domains
1990s
BRAIN
initiative
ldquodecade of
the brainrdquo
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
bull Electroencephalograph (EEG)
bull Electrocardiograph (EKG)
bull Galvanic skin response (GSR)
bull Pupilometry Eyetracking
Augmented Cognition overview
bull Goal Maximize operator cognitive performance
in dynamic complex operational environments
bull Approach Physiological-data based
assessment of operator cognitive state
ndash Detects predicts avoids overload to reduce
operator error and maximize effectiveness
bull Benefit Mitigate
negative effects of
cognitive overload
ndash Increase task
speed and
accuracy
ndash Improve critical
situation
understanding
Sensors
C2
System
User
SMART
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Next-Gen Wearable Sensors - Real-time physiological monitoring of body chemistries
Body chemistries and signature analysis through analytics will enable a wealth of physiological and cognitive assessments not previously possible
Orexin A alertness
Neuropeptide Y
depression
Cortisol stressDopamine amp Norepinephrin performance
bull Flexible conformal unobtrusive form factor
bull Real-time biomarker measurements
bull Correlations to physical and cognitive states
that affect performance
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
1st use case Aging Workers
Benefits
bull Deep fund of work-relevant knowledge
bull Can help mentor younger employees
bull May be willing to work part time saving employer costs
bull Health costs reduced when people stay cognitively active
bull High levels of engagement
Challenges
bull Slight cognitive declines begin in the 50s
bull Physical limitations may require accommodations
bull User acceptance usability of both wearable electronics and
assistiveaugmentative technologies
| 83252015
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
| 9
Aging Workers Need for DCs
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Aging Workforce in US
| 103252015
bull The Government Accountability Office projected in 2006 that
20 of the workforce would be aged 55 or older by 2015 [1]
bull Population research indicates that over 75 of baby boomers
are planning to work past retirement age [2]
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Aging Workers Need for DCs
| 113252015
bull Employers want to retain valuable older employees but both
lack knowledge about tools and strategies that can help
identify and accommodate for cognitive and physical changes
bull US universities in particular need to be prepared to
support an aging workforce
0
5
10
15
20
25
30
35
40
45
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Percentage aged 60 or over
World Average United States of America Repblic of Korea
Many other
countries are also
expecting aging
workforces eg
South Korea
httpdataunorgDataaspxq=aged
+over+60ampd=PopDivampf=variableID
3a33
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
DC for Smart Aging Opportunities
| 123252015
bull Seoul Korea government officials are interested in
supporting Smart Aging applied research
bull NSF Partnerships for Innovation Building Innovation
Capacity program
bull Smart Aging Service System (SASS) provides
older workers with personalized private
recommendations designed to help reduce health
risks and maintain or improve job performance
bull Enables older employees with valued skills and
experience to continue working
bull Reduces health costs by helping employees
take ownership of their healthcare
bull Provides a framework for developing
personalized health recommender systems
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Drexelrsquos multi-pronged approach
| 133252015
1 Faculty develop prototype that detects and mitigates against
most common changes in cognitive function (under NSF
PFIBIC)
2 CS PhD student builds Material Science Cognitive Assistant
3 Undergrad student(s) chart out the space of assistive
technologies that can support individuals with intellectual
disabilities
4 Faculty propose to develop new sensors smart aging
metrics data repository and analysis tools with IBM and Korea
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Digital Colleagues for Smart Aging Initial effort
Goal Keep aging University workforce healthy and engaged
even when faced with changes in any of three cognitive
functions memory attention processing speed
Approach Wearable and environmental sensors feed an
intelligent ldquoDigital Colleaguerdquo that a) recommends
accommodations and strategies to enable continued
contributions and b) provides health alerts and reminders
| 143252015
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Initial Prototype Sensor Suite
| 153252015
Commercially available products to be used in initial proof-of-concept prototype
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Proof-of-concept prototype
| 163252015
Digital Colleagues have four
major software components
1 The robust data collection
system integrates outputs
of sensors into a data store
that supports rich queries
2 The knowledge base
backend holds facts and
inferences
3 The Digital Colleague Algorithms component translates information
about an employee into recommendations for assessments andor
interventions
4 The dialog interaction module communicates with the employee
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
| 17
Sample recommendations
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
bull Drexel facultystaff
ndash Yvonne Michael Assoc Prof School of Public Health
ndash Ibiyonu Lawrence Clinical Prof Drexel Univ College of Medicine
ndash Marcello Balduccini Asst Prof College of Computing amp Informatics
ndash Gaurav Naik Sr Research Scientist Applied Informatics Group
bull Industry Partners
ndash Cognitive Compass (CEO Madelaine Sayko)
ndash General Electric (Research scientists Luis Tari amp Alfredo Gabaldon)
ndash Independence Blue Cross (Sr VP amp CIO Somesh Nigam)
ndash Evoke Neuroscience (CEO David Hagedorn)
ndash Abilities Inc subsid of Viscardi Center (CEO Jessica Swirsky)
| 18
DC for Smart Aging Team
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
There will nevertheless be a fairly long interim during
which the main intellectual advances will be made by
men and computers working together in intimate
associationhellip those years should be intellectually the
most creative and exciting in the history of mankind
Licklider JCR (1960) ldquoMan-Computer
Symbiosisrdquo IRE Transactions on Human
Factors in Electronics volume HFE-1
pages 4-11
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
| 20
Additional References
1 Tishman FM Van Looy S and Bruyegravere M 2012 ldquoEmployer Strategies for Responding to an Aging Workforcerdquo
National Technical Assistance and Research Center 2012 Report
httpwwwdolgovodeppdfNTAR_Employer_Strategies_Reportpdf
2 Saad L (2013) ldquoThree out of Four US Workers Plan to Work Past Retirement Agerdquo Gallup Economy Report May
23rd 2013 httpwwwgallupcompoll162758three-four-workers-plan-work-past-retirement-ageaspx
3 TIAA Cref - Aging Workforce Series Health Fitness and the Bottom Line July 2012 httpswwwtiaa-
creforgpublicpdfAgingWorkforceHealthandFitnesspdf
4 Levin Sharon G and Paula E Stephan ldquoAge and research productivity of academic scientistsrdquo Research in Higher
Education 1989 30(5) 531- 549
5 Soumlrensen LE Pekkonen MM Maumlnnikkouml KH Louhevaara VA Smolander J Aleacuten MJ ldquoAssociations between
work ability health-related quality of life physical activity and fitness among middle-aged menrdquo Applied Ergonomics Nov
2008 39(6)786-91 httpwwwncbinlmnihgovpubmed18166167
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
DC HCI Challenges ndash Sensor Technologies
bull Unobtrusive comfortable but ruggedized form-factors
ndash Robust when dusty wet tappedhit etc
bull Largely autonomous operation Should be able to forget
you are wearing sensorshellipbut
bull Could it be useful to cue wearers into a potential
health problem Under what circumstances
Configurable
bull Replacement notifications and failure indicators ndash
how delivered and to whom
bull Wearer ldquoResetrdquo option
bull Data download notification ndash to whom Privacy
issues
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Additional DC HCI Challenges to explore
bull Do participants know about aging-related cognitive decline
and how it may impact work
ndash How would participants want to interact with their own
health data
ndash What sorts of privacy safeguards would they expect
bull How and when would they like to provide information about
themselves that sensors canrsquot currently capture (eg job
satisfaction ratings)
bull What factors should influence how information is requested
and guidance is presented (eg individual preferences
current capabilities the types and degrees of limitations
types of recommendations) and how
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
| 23
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
| 24
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
Logistics
bull Determine pre-mission readiness
bull Maintenance of safety during mission
Commander monitoring
health of squad
Accelerated Learning HMI design
bull Customize training based on cognitive
states of trainees
bull Develop interfaces that support low
cognitive workload
Dynamic modifications to training
exercises to speed learning
Medical
bull Early Medical Problem Detection
bull Triage
bull Accident Medical SA
bull Recording of injury event amp treatment
for use at all echelons of care
Pilot showing symptoms of Hypoxia
Monitoring applications
Developing technologies to collect and take action on
health and readiness data of individuals
In-Field Injury
Screening
Commander amp
Medic SA Sys
Learning amp HMI
design
-Continuously
-With Near-Zero Footprint
