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A semi-supervised Hidden Markov model-based activity monitoring system

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5 Author(s)
Min Xu ; 2-212 Center for Sci. & Technol., Blue Highway LLC, Syracuse, NY, USA ; Long Zuo ; Iyengar, S. ; Goldfain, A.
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Most existing human activity classification systems require a large training dataset to construct statistical models for each activity of interest. This may be impractical in many cases. In this paper, we proposed a semi-supervised HMM based activity monitoring system, that adapts the HMM for a specific subject from a general model in order to alleviate the requirement of a large training data set. In addition, using two triaxial accelerometers, our system not only identifies simple events such as sitting, standing and walking, but also recognizes the behavior or a more complex activity by temporally linking the events together. Experimental results demonstrate the feasibility of our proposed system.

Published in:

Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2011-Sept. 3 2011