Multimed Tools Appl (2018) 77:31261–31280 https://doi.org/10.1007/s11042-018-6117-z A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer 1 1 R. Jansi & R. Amutha Received: 24 October 2017 /Revised: 6 March 2018 /Accepted: 8 May 2018 / Published online: 5 June 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 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 dimen- sionality 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. . . .
Multimedia Tools and Applications – Springer Journals
Published: Jun 5, 2018
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