Abstract
Ambient assisted living facilities provide assistance and care for the elderly, where it is useful to infer their daily activity for ensuring their safety and successful aging. In this work, we present an activity recognition system that classifies a set of common daily activities, where it is designed to be comfortable and non-intrusive, and is comprised of commercial, robust and well known devices. A hybrid model of Bayesian networks and support vector machines for activity recognition with calibration is proposed to provide a high recognition accuracy and fast adaptation for new users. On the data collected from 15 participants, we have compared our approach to other two ways of building activity recognition systems, and shown its superiority.
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Hong, JH., Ramos, J., Shin, C., Dey, A.K. (2013). An Activity Recognition System for Ambient Assisted Living Environments. In: Chessa, S., Knauth, S. (eds) Evaluating AAL Systems Through Competitive Benchmarking. EvAAL 2012. Communications in Computer and Information Science, vol 362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37419-7_12
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DOI: https://doi.org/10.1007/978-3-642-37419-7_12
Publisher Name: Springer, Berlin, Heidelberg
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