Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Activity Recognition Using Wearable Sensors for Elder Care
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 02
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
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With the increasing use of mobile devices as personal recording, communication and sensing tools, extracting the semantics of life activities through sensed data (photos, accelerometer, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engaging in a process of lifelogging. Lifelogging typically involves using a range of (wearable) sensors to capture raw data, to segment into discrete activities, to annotate and subsequently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity recording and management system called ZhiWo. By using a supervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are interpreted as life activity retrieval units for personal archives.