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

Challenges for Older Adults Physical Social/emotional Sensory Cognitive –Example: Alzheimers 65-74: 5% 75-84: 20% > 85: 50% intelligent wheelchairs elder-friendly and chat rooms programmable digital hearing aids Some of the technology also useful for younger individuals with cognitive impairment (e.g., TBI patients, developmentally disabled people)

Why Build Cognitive Orthotics? Cognitive impairment can impact performance of daily activities Can lead to decreased quality of life, and potentially institutionalization –Costly –Further decreases quality of life Goals –Improve performance of routine functional activities and thereby support longer aging-in-place –Reduce caregiver burden

Activity Cueing Guide an individual through steps in a sequential or conditional-branching process Work done both on ADLs/IADLs (e.g., handwashing, cooking) and on functional job tasks (e.g., janitorial) Handwashing Assistant [courtesy A. Mihailidis, U. Toronto]

Prospective Memory Aids Tend to be designed for less severely impaired individuals Provide them with personalized, adaptive reminders about daily activities On the market: glorified alarm clocks! –Exception: PEAT

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

Autominder Example Req/OptActivityAllowedExpectedObserved Rtoilet use10:45- 11:05 Rlunch12:00- 12:45 OTV14:00- 14:30 10:55 Rtoilet use 13:55- 14:15 REMIND 12:25 REMIND 13:55 12:28

Robot Platform Nomadic Technologies Scout II w/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]

Ubicomp Platform Handheld or wearable device –Currently: HP iPaq Deployed in a smart environment with multiple sensors (ubiquitous computing environment)

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

Plan Manager: What should the client do? Maintains up-to-date record of clients planned activities –Eating, hydrating, toileting, medicine-taking, exercise, social activities, doctors appointments, etc. Updates plan and propagates constraints when –New planned activity added. –Existing activity modified or deleted. –Planned activity performed. –Critical time bounds passed. Models plans as Disjunctive Temporal Problems and uses AI planning and CSP technology for updating.

Client Modeler: What is the client doing? Given information: –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 probability that various events have occurred –that the client has begun breakfast Uses Bayesian reasoning technology, addressing limitations of previous approaches to handle complex and dynamic temporal relations

Intelligent Reminder Generation: What should Autominder do? Given a clients 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 Uses local search tools to incrementally refine reminder plans; also investigating reinforcement learning for adaptive interaction policies

Current Status System fully implemented Early version tested on Pearl at Longwood Elder Care Facility in Oakmont, PA Later version currently being tested on handhelds, without sensing/ with simple (RFID-based sensing), with TBI patients from U of M Med Rehab Clinic Larger scale wireless sensing technology being developed and integrated into Autominder in the lab, for field testing later this year

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

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The Plan Manager Maintains up-to-date record of clients planned activities and their execution status –Eating –Hydrating –Toileting –Medicine-taking –Exercise –Social activities –Doctors appointments –etc.

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.

Small Example Client Plan 1.New Activity 2.Mod/Deletion 3.Activity Execution 4.Passed Time Bound PLAN MANAGER :0 M S – L E :60 Take meds within 1 hour of lunch L E = 12:15 Lunch ended at 12: :15 M S 13:15 Take meds by 1:15

Client Modeler Plan Manager IntelligentReminderGenerator Client Plan Activity Info Inferred Activity Sensor Data Reminders Client Model Info Activity Info Preferences Plan Updates Client Model Autominder Architecture

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

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

Client Modeler Plan Manager IntelligentReminderGenerator Client Plan Activity Info Inferred Activity Sensor Data Reminders Client Model Info Activity Info Preferences Plan Updates Client Model Autominder Architecture

Intelligent Reminders Decides whether and when to issue reminders Given a clients 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

How Does it Work (Now)? LBD TV Midnight 8:0016:0012:00 LBD TV Midnight 8:0016:0012:00 LBD TV Midnight 8:0016:0012:00 8:30 12:32

How Will it Work? Use reinforcement learning to deduce an optimal reminding strategy Model the system as a Markov decision process that –Senses the environment –Decides what action to perform –Receives a payoff and then learn the best policy after repeated interactions

Current Status of Autominder 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 Systematic field-testing to begin momentarily with TBI patients

Many projects going on to develop technology to support (older) individuals with cognitive impairment With the potential to have a huge impact But still lots of issues to resolve: –A host of scientific questions and engineering challenges Sensor interpretation Interface design... –Question of cost and reimbursement structure –Privacy, privacy, privacy! Conclusions

Acknowledgements Autominder DTP/PM: –Ioannis Tsamardinos –Sailesh Ramakrishnan –Cheryl Orosz CM: –Dirk Colbry –Bart Peintner IRG: –Colleen McCarthy –Matt Rudary System Integration: –Laura Brown –Martina Gierke –Peter Schwartz –Joe Taylor Funders National Science Foundation Intel Corporation [Supporting Technology: DARPA, AFOSR] Pearl Sebastian Thrun, Mike Montemerlo, Joelle Pineau, Nick Roy Rest of the Nursebot Team Jacqueline Dunbar-Jacob, Sandra Engberg, Judy Matthews, Sara Keisler, Don Chiarulli, Jennifer Goetz