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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Distributed segmentation and classification of human actions using a wearable motion sensor network
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Allen Y. Yang, Shankar Sastry, Ruzena Bajcsy Department of EECS, University of California, Berkeley, USA
Sameer Iyengar, Shankar Sastry, Ruzena Bajcsy Department of EECS, University of California, Berkeley, USA
Philip Kuryloski, Department of ECE, Cornell University, USA
Roozbeh Jafari, Department of EE University of Texas, Dallas, USA
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 l1-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.
Citation:
Allen Y. Yang, Sameer Iyengar, Philip Kuryloski, Roozbeh Jafari, "Distributed segmentation and classification of human actions using a wearable motion sensor network," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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