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Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer

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Abstract

The current generation of portable mobile devices incorporates various types of sensors that open up new areas for the analysis of human behavior. In this paper, we propose a method for human physical activity recognition using time series, collected from a single tri-axial accelerometer of a smartphone. Primarily, the method solves a problem of online time series segmentation, assuming that each meaningful segment corresponds to one fundamental period of motion. To extract the fundamental period we construct the phase trajectory matrix, applying the technique of principal component analysis. The obtained segments refer to various types of human physical activity. To recognize these activities we use the k-nearest neighbor algorithm and neural network as an alternative. We verify the accuracy of the proposed algorithms by testing them on the WISDM dataset of labeled accelerometer time series from thirteen users. The results show that our method achieves high precision, ensuring nearly 96 % recognition accuracy when using the bunch of segmentation and k-nearest neighbor algorithms.

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Acknowledgments

This work is supported by the Russian Foundation for Basic Research (RFBR) under grant number 14-07-31326 and by the Ministry of Education and Science of the Russian Federation, RFMEFI60414X0041.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Inststitutskii per. 9, 141700, Dolgoprudny, Moscow Region, Russia

    Andrey D. Ignatov & Vadim V. Strijov

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  1. Andrey D. Ignatov
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  2. Vadim V. Strijov
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Correspondence to Andrey D. Ignatov.

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Ignatov, A.D., Strijov, V.V. Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer. Multimed Tools Appl 75, 7257–7270 (2016). https://doi.org/10.1007/s11042-015-2643-0

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  • DOI: https://doi.org/10.1007/s11042-015-2643-0

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