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      Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

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          Abstract

          Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l(1) minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.

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          Author and article information

          Journal
          IEEE Journal of Biomedical and Health Informatics
          IEEE J. Biomed. Health Inform.
          Institute of Electrical and Electronics Engineers (IEEE)
          2168-2194
          2168-2208
          May 2013
          May 2013
          : 17
          : 3
          : 553-560
          Article
          24592458
          © 2013
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