Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

• Published in: IEEE journal of biomedical and health informatics Vol. 17; no. 3; pp. 553 - 560
• Main Authors: Mi Zhang, Sawchuk, A. A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activities of Daily Living - classification; Adult; Algorithms; Compressed sensing; Dictionaries; Feature extraction; Female; human activity recognition; Humans; Male; Medical services; Middle Aged; Minimization; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Pattern Recognition, Automated - methods; pervasive healthcare; Reproducibility of Results; Sensors; sparse representation; Studies; Training; Vectors; wearable computing; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2013.2253613

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.