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Intelligent Environments Environments that use technology
to assist inhabitants by automating task components
Aimed at improving inhabitants’ experience and task performance
NOT: large number of electronic gadgets
Objectives ofIntelligent Environments Improve Inhabitant experience:
Optimize inhabitant productivity Minimize operating costs Improve comfort Simplify use of technologies Ensure security Enhance accessibility
Requirements forIntelligent Environments Acquire and apply knowledge about
tasks that occur in the environment Automate task components that
improve efficiency of inhabitant tasks
Provide unobtrusive human-machine interfaces
Adapt to changes in the environment and of the inhabitants
Ensure privacy of the inhabitants
Examples of Intelligent Environments
Intelligent Workspaces Automatic note taking
Simplified information sharing
Optimized climate controls
Automated supply ordering
Examples of Intelligent Environments
Intelligent Vehicles
Location-aware navigation systems
Task-specific navigation
Traffic-awareness
Examples of Intelligent Environments
Smart Homes Optimized climate and light controls Item tracking and automated ordering
for food and general use items Automated alarm schedules to match
inhabitants’ preferences Control of media systems
Existing Projects
Academic Georgia Tech Aware Home MIT Intelligent Room Stanford Interactive Workspaces UC Boulder Adaptive House UTA MavHome Smart Home TCU Smart Home
Existing Projects
Industry
General Electric Smart Home
Microsoft Easy Living
Philips Vision of the Future
Verizon Connected Family
Georgia Tech Aware Home Perceive and assist occupants Aging in Place (crisis support) Ubiquitous sensing
Scene understanding, object recognition Multi-camera, multi-person tracking Context-based activity
Smart floor http://www.cc.gatech.edu/fce/ahri/
MIT Intelligent Room
Support natural interaction with room Speech-based information access Gesture recognition Movement tracking Context-aware automation
http://www.ai.mit.edu/projects/aire/
Stanford Interactive Workspaces Large wall and tabletop interactive
displays Scientific visualization Mobile computing devices Computer-supported cooperative
work Distributed system architectures http://iwork.stanford.edu/
UC Boulder Adaptive House Infer patterns and predict actions Machine learning for automation HVAC, water heater, lighting control Goals:
Reduce occupant manual control Improve energy efficiency
http://www.cs.colorado.edu/~mozer/house/
UTA MavHome Smart Home Learning of inhabitant patterns Learn optimal automation
strategies Goals
Maximize comfort and productivity Minimize cost
Ensure security http://ranger.uta.edu/smarthome/
TCU Smart Home
Inhabitant Prediction
Smart entertainment control
Smart kitchen recipe services
Household staff modeling http://personal.tcu.edu/~lburnell/
crescent/crescent.html
General Electric Smart Home Appliance control interfaces Climate control Energy management devices Lighting control Security systems Consumer Electronics Bus (CEBus) http://www.geindustrial.com/cwc/
home
Microsoft Easy Living Camera-based person detection and
tracking Geometric world modeling for context Multimodal sensing Biometric authentication Distributed systems Ubiquitous computing http://research.microsoft.com/easyliving/
Philips Vision of the Future
Less obtrusive technology
Technology devices Interactive wallpaper
Control wands
Intelligent garbage can
http://www.design.philips.com/vof
Verizon Connected Family
Remote monitoring of the home
Entry authentication
Integrated, pervasive
communications
Centralized data management
Challenges inIntelligent Environments
Home design and sensor layout Communication and pervasive
computing Natural interfaces Management of available data Capture and interpretation of tasks Decision making for automation Robotic control Large-scale integration Inhabitant privacy
Sensors
How many and what type?
How to interpret sensor data?
How to interface with sensors?
Are sensors active or passive?
Communications
What medium and protocol?
How to handle bandwidth limitations?
What structure does the communication
infrastructure have?
Data Management
How to store all the data?
What data is stored?
How is data distributed to the
pervasive computing infrastructure?
Prediction & Decision Making How to extract and represent
inhabitants’ task patterns? What patterns should be
maintained? How to determine the actions to
automate? To what level should tasks be
automated?
Automation
How are the tasks automated?
How are actuators controlled?
How is safety ensured?
System Integration How to achieve extensibility? Should the system be centralized
or decentralized? How to integrate existing
technology components? How to make integration and
interface intuitive?
Privacy
How to ensure that inhabitant
information remains private?
What data should be gathered?
How should personal data be
maintained and used?
Course Topics Sensing Networking Databases Prediction and Data Mining Decision Making Robotics Privacy Issues
Example Scenario
Smart kitchen item tracking Sense and monitor items in the kitchen Predict usage patterns Automatically generate shopping lists
based on usage patterns Automatically retrieve replacement
items
