IEEE Pervasive Computing
Algorithm to automatically detect abnormally long periods of inactivity in a home
Proceedings of the 1st ACM SIGMOBILE international workshop on Systems and networking support for healthcare and assisted living environments
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Transmission of patient vital signs using wireless body area networks
Mobile Networks and Applications - Special issue on Wireless and Personal Communications
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Two sensing options are examined for their potential to improve the sensitivity of a system that detects periods of inactivity in the homes of elderly persons. A previous prototype used passive infrared motion sensors and door sensors combined with a learning algorithm to detect periods of unusual inactivity such as late wake-ups or the aftermath of a fall. This system worked as intended but suffered from low sensitivity, especially at nighttime, since the motion sensors were not able to distinguish a fall in the bedroom from a person getting into bed. Experiments with a worn accelerometer and with bed and chair occupancy sensors suggest that both can dramatically improve system sensitivity. The optimal solution may depend on the users' activity level, living area size, and willingness to wear a device.