Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 17, Issue 3, Pages 553-560Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2013.2253613
Keywords
Compressed sensing; human activity recognition; pervasive healthcare; sparse representation; wearable computing
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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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