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Classification of human physical activity based on the raw accelerometry data via spherical coordinate transformation

Michał Kos, Małgorzata Bogdan, Nancy W. Glynn, Jaroslaw Harezlak
doi: https://doi.org/10.1101/686519
Michał Kos
1Department of Mathematics, University of Wrocław, Poland
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Małgorzata Bogdan
1Department of Mathematics, University of Wrocław, Poland
2Department of Statistics, Lund University, Sweden
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Nancy W. Glynn
3Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Jaroslaw Harezlak
4Department of Epidemiology and Biostatistics, Indiana University School of Public Health in Bloomington, Bloomington, Indiana, USA
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  • For correspondence: harezlak@iu.edu
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Abstract

Human health is strongly associated with person’s lifestyle and levels of physical activity. Therefore, characterization of daily human activity is an important task. Accelerometers have been used to obtain precise measurements of body acceleration. Wearable accelerometers collect data as a three-dimensional time series with frequencies up to 100Hz. Using such accelerometry signal, we are able to classify different types of physical activity.

In our work, we present a novel procedure for physical activity classification based on the raw accelerometry signal. Our proposal is based on the spherical representation of the data. We classify four activity types: resting, upper body activities (sitting), upper body activities (standing) and lower body activities. The classifier is constructed using decision trees with extracted features consisting of spherical coordinates summary statistics, moving averages of the radius and the angles, radius variance and spherical variance.

The classification accuracy of our method has been tested on data collected on a sample of 47 elderly individuals who performed a series of activities in laboratory settings. The achieved classification accuracy is over 90% when the subject-specific data are used and 84% when the group data are used. Main contributor to the classification accuracy is the angular part of the collected signal, especially spherical variance. To the best of our knowledge, spherical variance has never been previously used in the analysis of the raw accelerometry data. Its major advantage over other angular measures is its invariance to the accelerometer location shifts.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 28, 2019.
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Classification of human physical activity based on the raw accelerometry data via spherical coordinate transformation
Michał Kos, Małgorzata Bogdan, Nancy W. Glynn, Jaroslaw Harezlak
bioRxiv 686519; doi: https://doi.org/10.1101/686519
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Classification of human physical activity based on the raw accelerometry data via spherical coordinate transformation
Michał Kos, Małgorzata Bogdan, Nancy W. Glynn, Jaroslaw Harezlak
bioRxiv 686519; doi: https://doi.org/10.1101/686519

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