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Using rhythm awareness in long-term activity recognition

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3 Author(s)
Van Laerhoven, K. ; Tech. Univ. Darmstadt, Darmstadt ; Kilian, D. ; Schiele, B.

This paper reports on research where users' activities are logged for extended periods by wrist-worn sensors. These devices operated for up to 27 consecutive days, day and night, while logging features from motion, light, and temperature. This data, labeled via 24-hour self-recall annotation, is explored for occurrences of daily activities. An evaluation shows that using a model of the users' rhythms can improve recognition of daily activities significantly within the logged data, compared to models that exclusively use the sensor data for activity recognition.

Published in:

Wearable Computers, 2008. ISWC 2008. 12th IEEE International Symposium on

Date of Conference:

Sept. 28 2008-Oct. 1 2008