Title | ||
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Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors |
Abstract | ||
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Detecting human activity independent of intensity is essential in many applications, primarily in calculating metabolic equivalent rates and extracting human context awareness. Many classifiers that train on an activity at a subset of intensity levels fail to recognize the same activity at other intensity levels. This demonstrates weakness in the underlying classification method. Training a classifier for an activity at every intensity level is also not practical. In this paper, we tackle a novel intensity-independent activity recognition problem where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary. We propose a new robust stochastic approximation framework for enhanced classification of such data. Experiments are reported using two clustering techniques, K-Means and Gaussian Mixture Models. The stochastic approximation algorithm consistently outperforms other well-known classification schemes which validate the use of our proposed clustered data representation. We verify the motivation of our framework in two applications that benefit from intensity-independent activity recognition. The first application shows how our framework can be used to enhance energy expenditure calculations. The second application is a novel exergaming environment aimed at using games to reward physical activity performed throughout the day, to encourage a healthy lifestyle. |
Year | DOI | Venue |
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2014 | 10.1109/JBHI.2013.2287504 | Biomedical and Health Informatics, IEEE Journal of |
Keywords | DocType | Volume |
Gaussian processes,biomedical equipment,computer games,gait analysis,medical computing,mixture models,pattern classification,pattern clustering,sensors,stochastic processes,Gaussian mixture models,K-mean models,classification method,clustered data representation,clustering algorithms,energy expenditure calculations,exergaming,human activity detection,human context,intensity levels,intensity-independent activity recognition,intensity-independent activity recognition problem,metabolic equivalent rates,physical activity,robust activity recognition framework,stochastic approximation algorithm,wearable sensors,Classification,clustering,energy expenditure (EE),exergaming,intensity-varying activity,mixture models,stochastic approximation model | Journal | 18 |
Issue | ISSN | Citations |
5 | 2168-2194 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Nabil Alshurafa | 1 | 134 | 19.65 |
Wenyao Xu | 2 | 615 | 77.06 |
Jason J. Liu | 3 | 78 | 8.41 |
Ming-Chun Huang | 4 | 140 | 21.58 |
Bobak Mortazavi | 5 | 45 | 8.38 |
Christian K Roberts | 6 | 1 | 0.36 |
Majid Sarrafzadeh | 7 | 3103 | 317.63 |