NDLI: Distributed segmentation and classification of human actions using a wearable motion sensor network

Content Provider	IEEE Xplore Digital Library
Author	Yang, A.Y. Iyengar, S. Sastry, S. Bajcsy, R. Kuryloski, P. Jafari, R.
Copyright Year	2008
Description	Author affiliation: Dept. of EECS, Univ. of California, Berkeley, CA (Yang, A.Y.; Iyengar, S.; Sastry, S.; Bajcsy, R.)
Abstract	We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. We show that the distribution of multiple action classes satisfies a mixture subspace model, one sub-space for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the representation. We further provide fast linear solvers to compute such representation via $l^{1}-minimization.$ Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework.
Starting Page	1
Ending Page	8
File Size	2252949
Page Count	8
File Format	PDF
ISBN	9781424423392
ISSN	21607508
DOI	10.1109/CVPRW.2008.4563176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accelerometers Base stations Wireless sensor networks Humans Sensor systems Computer networks Biomedical monitoring Distributed computing Wearable sensors Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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