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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.

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We present a study which evaluates the use of simple lowpower sensors for a long-term, coarse-grained detection of sleep postures. In contrast to the information-rich but complex recording methods used in sleep studies, we follow a paradigm closer to that of actigraphy by using a wrist-worn device that continuously logs and processes data from the user. Experiments show that it is feasible to detect nightly sleep periods with a combination of light and simple motion and posture sensors, and to detect within these segments what basic sleeping postures the user assumes. These findings can be of value in several domains, such as monitoring of sleep apnea disorders, and support the feasibility of a continuous home-monitoring of sleeping trends where users wear the sensor device uninterruptedly for weeks to months in a row.