Presentation on theme: "Technology to Support Individuals with Cognitive Impairment Martha E. Pollack Computer Science & Engineering University of Michigan."— Presentation transcript:
Technology to Support Individuals with Cognitive Impairment Martha E. Pollack Computer Science & Engineering University of Michigan
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
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
“Ubicomp” Platform Handheld or wearable device –Currently: HP iPaq Deployed in a “smart” environment with multiple sensors (ubiquitous computing environment)
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.
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” ----------------------------- 12:15 M S 13:15 “Take meds by 1:15”
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)
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?
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 P b -P a 10) Algorithm: Floyd-Warshall, polynomial time
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
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
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
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
How Does it Work? LBD TV Midnight 8:0016:0012:00 LBD TV Midnight 8:0016:0012:00 LBD TV Midnight 8:3016:0012:00 8:30 12:32 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
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
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