Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Daily Routine Recognition through Activity Spotting
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
Learning patterns of pick-ups and drop-offs to support busy family coordination
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Pattern Recognition Letters
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Improving activity recognition without sensor data: a comparison study of time use surveys
Proceedings of the 4th Augmented Human International Conference
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
Human activity recognition using social media data
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Activity recognition for creatures of habit
Personal and Ubiquitous Computing
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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.