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Designing a robust activity recognition framework for health and exergaming using wearable sensors.

Publication ,  Journal Article
Alshurafa, N; Xu, W; Liu, JJ; Huang, M-C; Mortazavi, B; Roberts, CK; Sarrafzadeh, M
Published in: Ieee Journal of Biomedical and Health Informatics
September 2014

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.

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Published In

Ieee Journal of Biomedical and Health Informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

September 2014

Volume

18

Issue

5

Start / End Page

1636 / 1646

Related Subject Headings

  • Young Adult
  • Video Games
  • Stochastic Processes
  • Monitoring, Ambulatory
  • Humans
  • Human Activities
  • Exercise Therapy
  • Cluster Analysis
  • Algorithms
  • Adult
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Alshurafa, N., Xu, W., Liu, J. J., Huang, M.-C., Mortazavi, B., Roberts, C. K., & Sarrafzadeh, M. (2014). Designing a robust activity recognition framework for health and exergaming using wearable sensors. Ieee Journal of Biomedical and Health Informatics, 18(5), 1636–1646. https://doi.org/10.1109/jbhi.2013.2287504
Alshurafa, Nabil, Wenyao Xu, Jason J. Liu, Ming-Chun Huang, Bobak Mortazavi, Christian K. Roberts, and Majid Sarrafzadeh. “Designing a robust activity recognition framework for health and exergaming using wearable sensors.Ieee Journal of Biomedical and Health Informatics 18, no. 5 (September 2014): 1636–46. https://doi.org/10.1109/jbhi.2013.2287504.
Alshurafa N, Xu W, Liu JJ, Huang M-C, Mortazavi B, Roberts CK, et al. Designing a robust activity recognition framework for health and exergaming using wearable sensors. Ieee Journal of Biomedical and Health Informatics. 2014 Sep;18(5):1636–46.
Alshurafa, Nabil, et al. “Designing a robust activity recognition framework for health and exergaming using wearable sensors.Ieee Journal of Biomedical and Health Informatics, vol. 18, no. 5, Sept. 2014, pp. 1636–46. Epmc, doi:10.1109/jbhi.2013.2287504.
Alshurafa N, Xu W, Liu JJ, Huang M-C, Mortazavi B, Roberts CK, Sarrafzadeh M. Designing a robust activity recognition framework for health and exergaming using wearable sensors. Ieee Journal of Biomedical and Health Informatics. 2014 Sep;18(5):1636–1646.

Published In

Ieee Journal of Biomedical and Health Informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

September 2014

Volume

18

Issue

5

Start / End Page

1636 / 1646

Related Subject Headings

  • Young Adult
  • Video Games
  • Stochastic Processes
  • Monitoring, Ambulatory
  • Humans
  • Human Activities
  • Exercise Therapy
  • Cluster Analysis
  • Algorithms
  • Adult