Toward High-level Activity Recognition from Accelerometers on Mobile Phones

Sozo Inoue, Yuichi Hattori,
Proc. IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2011)
(Not Available)
(Not Available)
7 pages
Dalian, China
In this paper, we propose an unsupervised method for multi-level segmentation, which could be used for a pre- process of non-sequential activity recognition, and could con- struct a high-level activity recognition using accelerometers on mobile phones. We extend single-level segmentation to multi- level by sweeping the temporal parameter. To confirm the valid- ity of our approach. we pursued the experiment of gathering accelerometer data of real nursing in a hospital. After the experiment and multi-level segmentation, we confirmed several phenomena to imply the validity of multi-level segmentation such that sequence seems to be properly segmented fitting to the annotations transcribed from the voice, that there are peaks of lower-level segment boundaries without higher-level boundaries, and that higher-level boundaries are not lower- level boundaries.

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