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A naïve Gaussian Bayes classifier for detection of mental activity in gait signature

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  • Deepak Joshi
  • A. Mishra
  • Sneh Anand

Abstract

A probabilistic modelling is presented to detect mental activity from gait signature recorded from healthy subjects. The proposed scheme is based on principal component analysis with reduced feature dimension followed by a naïve Gaussian Bayes classifier. The leave-one-out cross-validation shows the detection accuracy of 94% with specificity and sensitivity of 96% and 98.3%, respectively. The research has a potential application in the prevention of elderly risk falls, lie detection and rehabilitation among Parkinson's patients.

Suggested Citation

  • Deepak Joshi & A. Mishra & Sneh Anand, 2012. "A naïve Gaussian Bayes classifier for detection of mental activity in gait signature," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 15(4), pages 411-416.
    • Handle: RePEc:taf:gcmbxx:v:15:y:2012:i:4:p:411-416
    DOI: 10.1080/10255842.2010.539562
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    1. Thomas B. L. Kirkwood, 2008. "Healthy old age," Nature, Nature, vol. 455(7214), pages 739-740, October.
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