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Published in:
2011 | OriginalPaper | Chapter
A Real-Time Living Activity Recognition System Using Off-the-Shelf Sensors on a Mobile Phone
Authors : Kazushige Ouchi, Miwako Doi
Published in: Modeling and Using Context
Publisher: Springer Berlin Heidelberg
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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.