Abstract
We propose an in-home living activity recognition method using only off-the-shelf sensors, namely, an accelerometer and a microphone, which are commonly applied in mobile phones. The proposed method firstly estimates a user’s movement condition roughly by acceleration sensing. Secondly, it classifies the working condition in detail by acoustic sensing when it estimates the condition to be working by acceleration sensing. We developed a prototype system to recognize the user’s living activity in real time and conducted two experiments to confirm the feasibility of the proposed method. As a result of the first experiment, three movement conditions; quiet, walking, and working, are classified with more than 95% accuracy by acceleration sensing. And it classified working into seven conditions with 85.9% accuracy by acoustic sensing. Moreover, the result of the second experiment shows that it is effective to adopt instance-based recognition according to the assumed application.
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Ouchi, K., Doi, M. (2011). A Real-Time Living Activity Recognition System Using Off-the-Shelf Sensors on a Mobile Phone. In: Beigl, M., Christiansen, H., Roth-Berghofer, T.R., Kofod-Petersen, A., Coventry, K.R., Schmidtke, H.R. (eds) Modeling and Using Context. CONTEXT 2011. Lecture Notes in Computer Science(), vol 6967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24279-3_24
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DOI: https://doi.org/10.1007/978-3-642-24279-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24278-6
Online ISBN: 978-3-642-24279-3
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