Abstract:
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 an...Show MoreMetadata
Abstract:
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.
Date of Conference: 28 September 2008 - 01 October 2008
Date Added to IEEE Xplore: 08 May 2009
Print ISBN:978-1-4244-2637-9