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Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

Published on May 1, 2013in IEEE Journal of Biomedical and Health Informatics4.22
· DOI :10.1109/JBHI.2013.2253613
Mi Zhang19
Estimated H-index: 19
(SC: University of Southern California),
Alexander A. Sawchuk27
Estimated H-index: 27
(SC: University of Southern California)
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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 l1 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.
  • References (27)
  • Citations (109)
References27
Newest
May 1, 2012 in BSN (Wearable and Implantable Body Sensor Networks)
#1Wenyao Xu (UCLA: University of California, Los Angeles)H-Index: 22
#2Mi Zhang (SC: University of Southern California)H-Index: 19
Last.Majid Surra (UCLA: University of California, Los Angeles)H-Index: 50
view all 4 authors...
37 CitationsSource
Jan 28, 2012 in IHI (International Health Informatics Symposium)
#1Mi Zhang (SC: University of Southern California)H-Index: 19
#2Alexander A. Sawchuk (SC: University of Southern California)H-Index: 27
84 CitationsSource
Dec 1, 2011 in ICMLA (International Conference on Machine Learning and Applications)
#1Mi Zhang (SC: University of Southern California)H-Index: 19
#2Alexander A. Sawchuk (SC: University of Southern California)H-Index: 27
19 CitationsSource
#1Mi Zhang (SC: University of Southern California)H-Index: 19
#2Alexander A. Sawchuk (SC: University of Southern California)H-Index: 27
123 CitationsSource
#1Jaishanker K. Pillai (UMD: University of Maryland, College Park)H-Index: 10
#2Vishal M. Patel (UMD: University of Maryland, College Park)H-Index: 38
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196 CitationsSource
#1Mohammad H. Mahoor (DU: University of Denver)H-Index: 22
#2Mu Zhou (DU: University of Denver)H-Index: 12
Last.Jeffrey F. Cohn (University of Pittsburgh)H-Index: 79
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65 CitationsSource
Sep 1, 2010 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Adil Mehmood Khan (Kyung Hee University)H-Index: 15
#2Young-Koo Lee (Kyung Hee University)H-Index: 31
Last.Tae-Seong Kim (Kyung Hee University)H-Index: 26
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321 CitationsSource
#1Richard G. Baraniuk (Houston Methodist Hospital)H-Index: 87
#2Emmanuel J. Cand (California Institute of Technology)H-Index: 81
Last.Yi Ma (UIUC: University of Illinois at Urbana–Champaign)H-Index: 70
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88 CitationsSource
#1John Wright (UIUC: University of Illinois at Urbana–Champaign)H-Index: 41
#2Allen Y. Yang (University of California, Berkeley)H-Index: 30
Last.Yi Ma (UIUC: University of Illinois at Urbana–Champaign)H-Index: 70
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6,957 CitationsSource
#1Carsten Orwat (Institute for Technology Assessment and Systems Analysis (ITAS))H-Index: 7
#2Andreas Graefe (Institute for Technology Assessment and Systems Analysis (ITAS))H-Index: 15
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