Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors

Nabil Alshurafa, Wenyao Xu, Jason J. Liu, Ming Chun Huang, Bobak Mortazavi, Christian K. Roberts, Majid Sarrafzadeh

Research output: Contribution to journalArticlepeer-review

75 Scopus citations


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.

Original languageEnglish (US)
Article number6648432
Pages (from-to)1636-1646
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
StatePublished - Sep 2014


  • Classification
  • clustering
  • energy expenditure (EE)
  • exergaming
  • intensity-varying activity
  • mixture models
  • stochastic approximation model

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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