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Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31261–31280 | Cite as

A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer

  • R. Jansi
  • R. Amutha
Article

Abstract

Human activity recognition using wearable body sensors plays a vital role in the field of pervasive computing. In this paper, we present human activity recognition framework using compressive classification of data collected from a tri-axial accelerometer sensor. Inspired by the theories of random projection, we propose a novel chaotic map for dimensionality reduction of the accelerometer raw data. This framework also involves extraction of time and frequency domain features from the compressed data. These features are used for human activity recognition using a sparse based classifier. Thus, a simultaneous dimension reduction and classification approach is presented in this paper. We experimentally validate the effectiveness of our proposed framework by recognizing 8 common daily human activities performed by 15 subjects of varying age groups. Our proposed framework achieves superior performance in terms of specificity, precision, F-score and overall accuracy.

Keywords

Activity recognition Accelerometer Chaotic map Classification Compression 

Notes

Acknowledgements

The authors would like to thank all individuals who extended their support during data collection. We are also pleased to express our immense gratitude towards Dr. S. Radha, Professor and Head of the Department, Electronics and Communication Engineering, SSNCE, for the provision of productive research environment.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSSN College of EngineeringChennaiIndia

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