Technology to Support Individuals with Cognitive Impairment

1. Technology to Support Individuals with Cognitive Impairment Martha E. Pollack Computer Science & Engineering University of Michigan

2. Autominder • Model, update, and maintain the client’s plan • Including complex temporal and causal constraints • Monitor the client’s performance • Updating the plan as execution proceeds • Reason about what reminders to issue, and when • To most effectively ensure compliance, without sacrificing client independence

3. What should the client do? Activity Info What is the client doing? Technologies: Automated Planning, Constraint-Based Temporal Reasoning Plan Manager Client Modeler Plan Updates Sensor Data Technologies: Dynamic Bayesian Inference Is a reminder needed? Inferred Activity Technologies: Iterative Refinement Planning, Reinforcement Learning Client Model Client Plan Activity Info Client Model Info Intelligent Reminder Generator Reminders Preferences Autominder Architecture

4. 13:55-14:15 R toilet use Autominder Example 10:55 REMIND 12:25 12:28 REMIND 13:55

5. Robot Platform • Nomadic Technologies Scout IIw/custom-designed head • Multiple sensors: lasers, sonars, microphone, touchscreen, camera vision, wireless ethernet • Effectors: motion, speakers, display screen, facial expression “Pearl” [courtesy Carnegie Mellon Univ. Robotics Institute]

6. “Ubicomp” Platform • Handheld or wearable device • Currently: HP iPaq • Deployed in a “smart” environment with multiple sensors (ubiquitous computing environment)

7. The Plan Manager • Maintains up-to-date record of client’s planned activities and their execution status • Eating • Hydrating • Toileting • Medicine-taking • Exercise • Social activities • Doctors’ appointments • etc.

8. How Does it Work? • Models constraints on future actions • Lunch takes between 25 and 35 minutes • Take meds within one hour of finishing lunch • Watch the news at either 6pm or at 11pm • Performs efficient constraint processing when key events occur: • New planned activity added. • Existing activity modified or deleted. • Planned activity performed. • Critical time bounds passed.

9. Small Example PLAN MANAGER Client Plan :0  MS – LE  :60 “Take meds within 1 hour of lunch” LE = 12:15 “Lunch ended at 12:15” ----------------------------- 12:15  MS  13:15 “Take meds by 1:15” • New Activity • Mod/Deletion • Activity Execution • Passed Time Bound

10. Temporal Reasoning in AI An important task & exciting research topic, otherwise we would not be here • Temporal Logic • Temporal Networks • Qualitative relations: • Before, after, during, etc. • interval algebra, point algebra • Quantitative/metric relations: • 10 min before, during 15 min, etc. • Simple TP (STP), Temporal CSP (TCSP), Disjunctive TP (DTP)

11. Temporal Network: example Tom has class at 8:00 a.m. Today, he gets up between 7:30 and 7:40 a.m. He prepares his breakfast (10-15 min). After breakfast (5-10 min), he goes to school by car (20-30 min). Will he be on time for class?

12. Simple Temporal Network (STP) • Variable: Time point for an event • Domain: A set of real numbers (time instants) • Constraint: An edge between time points ([5, 10]  5Pb-Pa10) • Algorithm: Floyd-Warshall, polynomial time

13. Example A [10 15] C ? [5 10] B

14. Example A 15 -10 C -5 10 B

15. Example A 15 -10 C 10 -5 10 B

16. Example A 15 -10 C 10 0 -5 10 B

17. Example A [10 15] C [0 10] [5 10] B

18. Other Temporal Problems Temporal CSP:Each edge is a disjunction of intervals STP  TCSP Disjunctive Temporal Problem:Each constraint is a disjunction of edges STP  TCSP  DTP

19. Search to solve the TCSP/DTP • TCSP [Dechter] and DTP [Stergiou & Koubarakis] are NP-hard • They are solved with backtrack search • Every node in the search tree is an STP to be solved • An exponential number of STPs to be solved 

20. CM: Client Modeler • Given what can be observed • Sensor input: client moved to kitchen • Clock time: at 7:23 a.m. • Client plan: breakfast should be eaten between 7 and 8 • Model of previous actions: client has not yet eaten breakfast • Learned patterns: 82% of the time, client starts breakfast • between 7:10 and 7:25 • Reminder information: we issued a reminder at 7:21 • Infers what has been done • Client Activity: probability that client has begun breakfast

21. reminder kitchen start-breakfast Y Y .95 Y N .10 N Y .8 N N .03 breakfast reminder issued went to kitchen How Does it Work? • Models probabilistic relations among observations and actions started breakfast • Performs Bayesian update, extended to handle temporal relations • Asks for confirmation when needed!

22. Intelligent Reminders • Decides whether and when to issue reminders • Given a client’s plan and its execution status: • Easy to generate reminders • Remind at earliest possible time of each action • Harder to “remind well” • Maximize likelihood of appropriate performance of ADLs and other key activities • Facilitate efficient performance • Avoid annoying client • Avoid making client overly reliant

23. 8:30 8:00 8:00 12:00 12:00 12:00 16:00 16:00 16:00 B B B L L L D D D 12:00 12:00 12:00 Midnight Midnight Midnight TV TV TV How Does it Work? Use Preferred Time Avoid Conflicts • Initially: schedule reminders for earliest possible time • Apply “rewrite rules” to improve remders: • Used preferred times for reminders • Combine “near” reminders that are compatible • e.g.: “drink water” and “take pills” • Reschedule reminders for conflicting activities 8:30 12:32

24. V.0 (Autominder + Pearl) field-tested for client acceptability on Pearl at Longwood Elderly Care Facility in Oakmont, PA, summer, 2001 V.1 of Autominder implemented Java, Lisp on Wintel machines Data collection with three Oakmont residents completed summer 2002; with Ann Arbor TBI patient summer 2003 Current Status of Autominder

25. Key Challenges for Cognitive Orthotics • Technological • Advanced AI Techniques • HCI • Sensor Networks for Inference of Daily Activities • Mechanisms to Ensure Privacy and Security • Policy • Mechanisms to Ensure Privacy • Reimbursement Policies