Abstract
The eWatch is a wearable sensing, notification, and computing platform built into a wrist watch form factor making it highly available, instantly viewable, ideally located for sensors, and unobtrusive to users. Bluetooth communication provides a wireless link to a cellular phone or stationary computer. eWatch senses light, motion, audio, and temperature and provides visual, audio, and tactile notification. The system provides ample processing capabilities with multiple day battery life enabling realistic user studies. This paper provides the motivation for developing a wearable computing platform, a description of the power aware hardware and software architectures, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations. We then design an activity recognition and monitoring system that identifies the user’s activity in realtime using multiple sensors. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.
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Maurer, U., Rowe, A., Smailagic, A., Siewiorek, D. (2006). Location and Activity Recognition Using eWatch: A Wearable Sensor Platform. In: Cai, Y., Abascal, J. (eds) Ambient Intelligence in Everyday Life. Lecture Notes in Computer Science(), vol 3864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11825890_4
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DOI: https://doi.org/10.1007/11825890_4
Publisher Name: Springer, Berlin, Heidelberg
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