Learning Micro-Behaviors In Support of Cognitive Assistance

1. Medical Center and Caregivers Body scale Wearable pulse and oximetry sensor Harvard University Micro-ECG Motion sensors. Located in each room to track resident locations. Medical Automation Research Center Bed sensor. Uses an unobtrusive air bladder to record respiration and heart rates while resident is sleeping. Blood pressure and heart rate sensor MTS-310 (Crossbow) Indoor temperature and light sensors Poga wearable micro-mote Learning Micro-Behaviors In Support of Cognitive Assistance John A. Stankovic, Leo Selavo, Anthony D. Wood Department of Computer Science, University of Virginia AlarmNet is a wireless sensor network (WSN) system for smart health-care that opens up new opportunities for continuous health monitoring in assisted-living or residential facilities. It provides real-time (24/7) access to physiological and environmental data, and tracks long-term changes in behavioral patterns for cognitive assistance by exploiting large numbers of cheap sensors, cell phones, a high degree of heterogeneity, and special purpose hardware. I. System Overview IV. Assistive Feedback • Improving Quality of Life and Health Care • Continuous, real-time monitoring: • Nutrition and hygiene • Disease progression & management • Treatment compliance • Environmental conditions • Unobtrusive smart clothes • Support longitudinal studies • Detect at-risk medical situations • Trigger alerts to health care providers • Cognitive Assistance • Use many, cheap, wearable sensors • Detect individual’s macro- & micro-behaviors • Use in-situ devices for interaction • Maintain resident privacy and security • Intelligent PDAs and Cell phones provide: • Enhanced sensing and monitoring • Wide-area communications • Cognitive assistance interaction • Mobile personal storage • Unique ID for data association Pills Assisted Living Facility Video cameras Motion sensors Backbone nodes Motes (emplaced WSN) Wearable devices provide low-power, easily-accessible displays for system feedback of reminders and prompts, risk alerts, and current sensor data. II. Heterogeneous Sensing Hardware Large numbers of cheap sensors provide better activity classification—beyond typical ADLs—and reveal where cognitive assitance is most needed. Experimental Testbed Body networks help record micro-behaviors for activities such as walking, eating and stillness using five 2-axis accelerometers embedded in a jacket. V. Behavior Patterns A medical application monitors the Circadian Activity Rhythms (CAR) of a resident to extract high-level activity patterns and detect behavioral anomalies. III. Wireless Sensor Network Continuous Monitoring User Reminders AlarmNet also records precise micro-behaviors that constitute higher-layer behaviors. A significant deviation from one’s own norm may indicate cognitive decline. Tailored cognitive assistance can be offered to the resident, and is stored in the database with privacy protections. VI. Privacy Challenges Ambient Sensing Transience of Data. Most data collected by the system are transient physiological and activity data. They require real-time processing. Resource Constraints. Despite the large amount of transient data to be collected, WSN devices have very limited power and storage. This requires careful design of data aggregation and processing algorithms. Cross-Boundary Addressing. Data is streamed to back-end servers, where privacy schemes control how data is stored and who can access it. Policies must be consistent in the back and front-ends of the system. Transience of Privacy Preferences. Individual’s privacy preferences are dynamic, often depending on the current context and health conditions. They should be dynamic, adjustable, and adaptive in emergencies. Interaction and Viewing Analysis and Feedback