Algorithm to automatically detect abnormally long periods of inactivity in a home

An algorithm has been developed to automatically construct individual models of normal activity within a home using motion sensor data. Alerts can be generated when a period of inactivity exceeds a normal length for a particular residence. Alerting frequency has been optimized on a total of 650 days of real data from four homes of seniors who live independently. Results suggest that an inexpensive system that does not require the occupant to push any buttons or wear any devices could nonetheless alert within hours if a senior is unusually inactive. Further, such algorithms may facilitate widespread deployment of smart home technology to persons with different behavior patterns and home layouts by using automatic learning in place of potentially tedious manual configuration. Categories and Subject Descriptors J.3 [LIFE AND MEDICAL SCIENCES]: Health General Terms Algorithms, Experimentation, Human Factors Keywords Activity detection, outlier detection, automated learning, elder monitori...

• Type: Conference
• Year: 2007
• Where: MOBISYS
• Authors: Paul Cuddihy, Jenny Weisenberg, Catherine Graichen, Meena Ganesh
• Keywords: Keywords Activity Detection, MOBISYS 2007, Motion Sensor Data, Tedious Manual Configuration, Wireless Networks