NCSU/URO NSF ITR Progress(March 2007)

David Kaber, Mo-Yuen Chow, Rob St. Amant &Regina Stoll

Departments of Industrial & SystemsEngineering, Electrical & Computer Engineering,

and Computer Science

North Carolina State University

“Intelligent Human-Machine Interfaces & Control for HighlyAutomated Biochemical Screening Processes”

“Top-3” Outcomes in Focus Areas: Industrial & Systems Engineering (ISE):

1. Conducted cognitive task analyses with high-throughputscreening (HTS) system users and automation engineers.

2. Redesigned and prototyped usable HTS process interfaces.3. Developed high-level cognitive models (GOMSL models) for

testing new interfaces and comparing with “old” designs.

Computer Science (CS):1. Developed new technique for translation of AI planning

language to HCI/cognitive modeling language.2. Reviewed high-level cognitive modeling (GOMS) techniques.3. Developed approach to stochastic modeling of HTS user tasks.

Electrical & Computer Engineering (ECE):1. Developed Petri Net (PN) model of HTS process.2. Developed hybrid simulation (discrete event and system

dynamics) of HTS process using MATLAB SIMULINK.3. Developed novel, efficient HTS scheduling algorithm.

Overall Research Process Model: Links among subteam activities:

ECE provides accurate simulation of HTS process at URO togenerate data for interface prototype testing. ECE develops networkresource scheduling algorithm for multiple robot/device control.

ISE (or IE) develops prototype interfaces to be linked to processsimulation. ISE creates high-level cognitive models for representingoperator behavior and prelim. interface testing.

CS translates cognitive task analysis into low-level ACT-R models ofbiologist behavior in HTS operations.

ECE and CS assist ISE with usability evaluations of old and newprocess control interfaces.

Outcomes in URO Focus Areas: Systems Engineering and Life Science Automation (LSA):

System setup and operation for HTS (automated) experiments aswell as for manual screening procedures.

Developed automated enzymatic and cellular screening methods.

Process Information Technologies (PIT): Developed web-based Operator Information Management System

(OIMS) for storing physiological data during HTS experiments. Reviewed and developed mobile wireless physiological and

workload data acquisition systems. Developed data visualization tools for real-time examination of

occupational physiology data on operators.

Occupational Physiology (OP): Investigated physiological stress response to different types of HTS

experiment workload (manual and automated screening). Evaluated methods for assessing stress tolerance under standard

operating conditions (to be used as “baseline” data for evaluatingother conditions).

Details of ISE Subteam Work: Cognitive task analyses (CTA) and automation modeling:

Used GDTA (Goal-Directed Task Analysis) to identify biologistprocess goals, tasks, decisions and information requirements.

Developed AH (abstraction hierarchy) models of processautomation describing purposes, functions and components.

Compared results to formulate interface design recommendations.

Usable interface prototyping: Used existing usability heuristics, results of CTA and new

design metaphor (“cookbook”) to prototype control dialogs forHTS devices and method editing software interfaces.

Developed Java-based prototypes and conducted usabilityevaluations with expert biologists at URO.

Cognitive model development and testing of interfaces: Developed computational GOMSL models to describe

scripted user behavior with HTS process control software. Applied models to actual interface prototypes using new

cognitive modeling tool (EGLEAN compiler and simulator).

Details of ISE Subteam Work: Images of overall GDTA hierarchy and example

AH model for HTS device:1.

Find “hits” among a huge number of compounds as fast as possible and at low cost

1.1.

Adapt “bench-top” method to High-

throughput

Screening (HTS) line

1.2.

Ensure results of assay meet quality

criteria

1.3.Optimize assay

method for efficiency

1.4.

Conduct accurate analysis of data from

assay

1.5.

Generate understandable

reports of results for

clients

1.2.1.

Identify criteria for quality factors

1.2.2.

Calculate quality statistics based on

sample data

1.2.3.Compare test

statistics with

criteria

1.2.4.

Assess quality of experimental runs

1.3.1.

Avoid bottlenecks

1.3.2.

Determine optimal

(plate) batch size

1.3.3.Establish optimal

sequence of steps

in assay

1.3.4.Optimize pipetting

steps

1.3.5.

Identify available

work-in-process storage space

1.4.1.

Establish criterion

activity level for identifying “hit”

1.4.2.Use basic statistical

analysis to identify and address outliers

in data

1.4.3.

Determine enzyme

activity levels (e.g., Trypsin) (conduct

data analysis)

1.4.4.

Determine

variation in enzyme activity levels

1.4.5.Decide whether

test compound is active

1.5.1.Address customer

information needs for decisions on

test compounds

(i.e., to support determination of

whether further investigation of

compounds is

needed)

1.1.1.

Identify steps that need to be

performed as part of (automated version of ) assay

1.1.2.

Identify appropriate micro -plate types for assay

1.1.3.Establish plate configuration to

achieve statistically valid results

1.1.4.Identify automated devices to use

to perform steps of assay

1.1.5.

Identify time critical steps as part

of assay as basis for sequencing steps

1.1.6.Identify feasible sequences of steps

in assay that allow for successful

execution of experiment (i .e ., Is there any flexibility in sequence of

steps in method ?)

1.1.7.

Adapt manual pipetting steps to automated version of assay

1.1.8.

Design measurement approach to facilitate analysis of inhibition of

enzyme activity by sample

compounds

1.1.9.Develop program for assay method

using HTS line control software

(e.g. Beckman Coulter -SAMI )

1.1.10.

Ensure reproducibility of results from HTS line Specific configuration of bar coding system components

Rotating feed motor

Feed reel for labels

Take-up reel for labelsFeed reel for foil

Take-up reel for foil

Reel position sensors

Process of system

initialization

Process of printing

bar code

Process of applying

bar code

Process of reading

bar code

Printer

Foil feeder

Vacuum gripper

Plate holder

Laser scanner

Manual entry keypad

LCD display

Process of verifying

available paper/foil Process of moving plate

holder to home position Process of paper/foil feed

Process of applying thermal ink to label

Process of removing label

from backing paperProcess of locating label position

and blowing label on micro-plateProcess of verifying label

Process of activating

reader (scanner)Process of moving

plate for scanning Process of positioning

plate holder (begin/end)

Thermal print head

Vertical translation motor

Laser diode

Rotating (scan) mirror

Receiver (photo diode)

Vacuum pump

Rotation motor

Translational motor

Rotational control motor

Translational control motor

Label paper feeder

Label and read micro-plates

Assign ID to,

and recognize,

micro-plates

RS-232 C connection

CPU

RS-232C connection to PC

Apply and read

controller

CPU

RS-232C connection to PC

RS232C to printer

GDTA forHTS Process

AH Model forBarcoder Device

Details of ISE Subteam Work: Images of old HTS process control interfaces:

Device-orientedprogramming of line

Original interfacerequires recall ofmachine language

Details of ISE Subteam Work: Images of HTS control interface prototypes:

Process-orientedprogramming of line

Redesigned interfacetaps biologist recognitionof familiar information

Details of ISE Subteam Work: Image of GOMSL model simulation in run-time

and application to actual HTS control interface:

GOMS codedebugging window

GOMS modeloutput window Java-based interface

presented to model(included system state info,features for user action).

Details of ISE Subteam Work: Usability evaluations:

5 expert biologists at CELISCA (30-40yrs.) and GOMSLmodels of human operators.

Tasks: (1) programming HTS method; (2) selecting andconfiguring micro-plate barcode labels.

Evaluated old and new interfaces in repeated tests. Dependent measures - Task completion time, errors and

usability ratings. Findings:

GOMSL model output not sign. different from actual operatorprocess behaviors and times (for 4 subtasks with oldinterface, 3-of-4 subtasks with new interface).

Expert task performance time with new interface comparableto old interface (even with limited training).

Significant reductions in errors with new interface (25% dec.). Biologist usability ratings for new interfaces (mean=4.0/5.0)

greater than for old interfaces (mean=3.25/5.0).

Major Publications in Focus Area: Industrial & Systems Engineering:

Kaber, D. B., Segall, N. & Green, R. (accepted and in revision).Metaphor-based design of high-throughput screening processinterfaces. Submitted to Int. J. of Usability Studies.

Kaber, D. B., Segall, N., Green, R. S., Entzian, K. & Junginger, S.(in press). Using multiple cognitive task analysis methods forsupervisory control interface design in high-throughput biologicalscreening processes. To appear in the Int. J. of CognitiveTechnology & Work.

Segall, N., Green, R. S. & Kaber, D. B. (2006). User, robot andautomation evaluations in high-throughput biological screeningprocesses. In Proc. of the 2006 ACM Conference on Human-robotInteraction (HRI 06) (pp. 274-281). New York: ACM.

Details of CS Subteam Work: Automated language translation tools:

Created compiler for converting action modeling formalismsin PDDL to operators in ACT-R.(PDDL useful for formalizing results of CTA. New compiler

provides pathway to transform CTA to cognitive model.) Currently developing suite of translation tools. Support and interest in cognitive modeling community.

Survey of cognitive modeling literature: First review of modeling methods research in 10 years. Focus on modern applications, off desktop.

Stochastic modeling of user tasks: Developed stochastic and time-dependent models of user

task performance using Markov chains. Developed approach for using cascading Markov models for

inferring HTS operator goal states. Currently in design and implementation stage.

Details of CS Subteam Work: Images of PDDL code and corresponding ACT-R

model translation:

(define (domain hanoi) (:requirements :strips) (:predicates (clear ?x) (on ?x ?y) (smaller ?x ?y)) (:action move :parameters (?disc ?from ?to) :precondition (and (smaller ?to ?disc) (on ?disc ?from) (clear ?disc) (clear ?to)) :effect (and (clear ?from) (on ?disc ?to) (not (on ?disc ?from)) (not (clear ?to)))))(define (problem hanoi4) (:domain hanoi) (:objects peg1 peg2 peg3 d1 d2 d3 d4) (:init (smaller peg1 d1) (smaller peg1 d2) (smaller peg1 d3) (smaller peg1 d4) (smaller peg2 d1) (smaller peg2 d2) (smaller peg2 d3) (smaller peg2 d4) (smaller peg3 d1) (smaller peg3 d2) (smaller peg3 d3) (smaller peg3 d4) (smaller d2 d1) (smaller d3 d1) (smaller d3 d2) (smaller d4 d1) (smaller d4 d2) (smaller d4 d3) (clear peg2) (clear peg3) (clear d1) (on d4 peg1) (on d3 d4) (on d2 d3) (on d1 d2)) (:goal (and (on d4 peg3) (on d3 d4) (on d2 d3) (on d1 d2))))

(p move =goal> isa move-disk test 0 from =from to =to disk =size =disk> isa disk size =size==> =disk> peg =to !pop!)

Major Publications in Focus Area: Computer Science:

Ritter, F.E., Kukreja, U. & St. Amant, R. (in press). Including a model ofvisual processing with a cognitive architecture to model a simpleteleoperation task. Journal of Cognitive Ergonomics and Decision-Making.

St. Amant, R., Horton, T.E. & Ritter, F.E. (in press). Model-based evaluationof expert cell phone menu interaction. ACM Transactions on Computer-Human Interaction.

Ritter, F.E., Van Rooy, D., St. Amant, R. & Simpson, K (2006). Providinguser models with direct access to computer interfaces: An exploratory studyof a simple human-robot interface. IEEE Transactions on Systems, Man, andCybernetics (Part A), 36(3):592–601, 2006.

St. Amant, R., McBride, S.P. & Ritter, F. E. (2006). An AI planningperspective on abstraction in ACT-R modeling: Toward an HLBR languagemanifesto. In 13th Annual ACT-R Workshop (pp. 72–76).

St. Amant, R., McBride, S.P. & Ritter, F. E. (2006). AI support for buildingcognitive models. In Proc. of the 21st National Conference on ArtificialIntelligence (AAAI) (Nectar Track, pp. 1663–1666). Menlo Park, CA: AAAIPress.

Details of ECE Subteam Work: PN modeling:

Studied HTS process as discrete event system. Investigated scheduling algorithm for HTS process. Provided high-level, event-driven structure for simulator.

Hybrid simulation: Generated data on process under faulty/normal conditions. Providing data for presentation through prototype control

interfaces for GOMSL model evaluation (ISE Team). Process scheduling algorithms:

Used to schedule HTS experiments before running process. Can be used to reschedule in real-time when faults occur.

Details of ECE Subteam Work:

CAD model andsimulation of HTSprocess line.

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Image of PN Model of HTS process.

Details of ECE Subteam Work: Image of SIMULINK simulation of HTS process:

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Details of ECE Subteam Work: Flowchart of HCH algorithm for HTS scheduling:

Major Publications in Focus Area: Electrical & Computer Engineering:

T. Hong, M.-Y. Chow, P. Haaland, D. Wilson, & R. Walker, "Scheduling a LifeScience High-Throughput Platform under Starvation Constraints Using TimedTransition Petri Nets and Heuristic Search," Proc. of IEEE ISIE07, Vigo, Spain, June4-7, 2007.

Z. Li & M.-Y. Chow, "Sampling Rate Scheduling and Digital Filter Co-design ofNetworked Supervisory Control System," Proc. of IEEE ISIE07, Vigo, June 4-7, 2007

T. Hong & M.-Y. Chow, “Timed Petri Nets Modeling of High-Throughput ScreeningProcess for Fault Study”, Proc. of IEEE IECON06, Paris, Nov. 6-10, 2006.

Z. Li & M.-Y. Chow, “Adaptive Multiple Sampling Rate Scheduling of Real-timeNetworked Supervisory Control System – Part I,” Proc. of IEEE IECON06, Paris,Nov. 6-10, 2006.

Z. Li & M.-Y. Chow, “Adaptive Multiple Sampling Rate Scheduling of Real-timeNetworked Supervisory Control System – Part II,” Proc. of IEEE IECON06, Paris,Nov. 6-10, 2006.

Z. Li, R. Vanijjirattikhan, M.-Y. Chow & Y. Viniotis, “Real-time IP Network TrafficDelay Estimation Model Design and Comparison with DSP Technology,” Proc. ofIECON05, Raleigh, NC, Nov. 6-10, 2005, pp. 2439 - 2444

W-L. (Danny) Leung, R. Vanijjirattikhan, Z. Li, L. Xu, T. Richards, B. Ayhan & M.-Y.Chow, “Intelligent Space with Time Sensitive Applications”, 2005 IEEE/ASME Int.Conference on Advanced Intelligent Mechatronics, Monterey, CA, 24-28 July, 2005.

Details of URO Work: Systems Engineering and LSA:

Developed ORCA and SAMI XXbased systems.

Screening methods applied torobotic lines at CELISCA.

Process Info Technologies (PIT): 24/7 individual operator data

recording/representation. Database storage of work-

related data. Occupational Physiology (OP):

Related degree of process autoto work efficiency and HRmeasure of workload.

Measured Cortisol in salivaunder various HTS workloadand auto conditions.

Major Publications by URO: Vilbrandt, R., Kreuzfeld, S., Stoll, R.: Flexible erfassung von belastungs-

und beanspruchungsparametern bei arbeitsmedizinischenfelduntersuchungen. Arbeitsmed.Sozialmed.Umweltmed. 41 (2006) 10,457-462.(Industrial field investigation of workload parameters on medical responses.)

Kumar, M., Weippert, M., Arndt, D., Kreuzfeld, S., Vilbrandt, R., Stoll, R.:Fuzzy evaluation of heart rate signals for mental stressassessment. IEEE Trans. on Fuzzy Systems Vol. 14 (2007) (in press).

Stoll, R., Kreuzfeld, S., Weippert, M., Vilbrandt, R., Stoll, N.: System forflexible field measurement of physiological data of operators working inautomated labs. J. Assn. Lab Auto. 11 (2007) 2, (in press).

Weippert, M., Kreuzfeld, S., Kumar, M., Kaber, D., Stoll, R.: Fuzzymodeling of mental effort using heart rate variability-data. Proc. 4th Int.Forum Life Science Automation, Hohe Düne, 14.-15.09.2006, 80.

Kumar, M., Stoll, N., Vilbrandt, R., Kaber, D., Stoll, R.: Fuzzy-baseddata interpretation – A tool for investigation of laboratory staff’s stresslevel. Proc. 4th Int. Forum Life Science Automation, 14.-15.09.2006,Hohe Düne, 69.

NCSU Students Graduated orFunded by ITR Grant:

Computer Science: Marivic Bonto-Kane (expected 2007-8; passed prelim.) Sean McBride (expected 2007-8)

Electrical & Computer Engineering: YiXin Cai (in progress) Tao Hong (in progress) Zheng Li (expected 2007; passed proposal defense) Rangsarit Vanijjirattikhan (expected 2007)

Industrial & Systems Engineering: Rebecca Green (expected 2008; passed prelim.) Sang-Hwan Kim (in progress) Noa Segall (2006; completed)

Faculty Service on InternationalStudent Dissertation Committees: Chow (NCSU ECE):

Thomas Krueger-Sundhaus, College of Computer Science andElectrical Engineering, University of Rostock (Ph.D., 2006).

Kaber (NCSU ISE): Thomas Roddelkopf, College of Computer Science and Electrical

Engineering, University of Rostock (Ph.D., 2006; completed). Ralf Schroder, College of Computer Science and Electrical

Engineering, University of Rostock (Ph.D., 2007; completed).

St. Amant (NCSU CS): Holger Dahl, College of Computer Science and Electrical

Engineering, University of Rostock (Ph.D., 2006).

Stoll (URO School of Medicine): Noa Segall, Department of Indus. & Sys. Engr., NC State

University (Ph.D., 2006; completed)

URO Students Graduated or Fundedby PEO (German ITR) Grant:

Beyond students graduated through jointcommittees, expected URO Ph.D. graduationsinclude: Mohit Kumar (expected 2007-8) for Habilitation Matthias Weippert (expected 2007) for Ph.D. Reinhard Villbrandt (expected 2008) for Ph.D. Sebastian Neubert (expected 2009) for Ph.D.

(Note: These students are supervised by R. Stoll, N. Stoll &K. Thurow through CELISCA.)