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Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

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Abstract

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.

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Acknowledgments

This study was supported by a grant from National Institutes of Health (R21HL093407) to develop novel approaches to monitor physical activity in children.

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Authors and Affiliations

  1. Wuhan University International School of Software, 37 Luoyu Road, Hongshan District, Wuhan, Hubei Province, China

    Xiang Ren & Rong Xie

  2. Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA, 02125-3393, USA

    Wei Ding & Yang Mu

  3. Department of Kinesiology, Recreation, and Sport Studies, The University of Tennessee, Knoxville, USA

    Scott E. Crouter

Authors
  1. Xiang Ren
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  2. Wei Ding
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  3. Scott E. Crouter
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  4. Yang Mu
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  5. Rong Xie
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Correspondence to Xiang Ren or Wei Ding.

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Ren, X., Ding, W., Crouter, S.E. et al. Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning. Appl Intell 45, 512–529 (2016). https://doi.org/10.1007/s10489-016-0773-3

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  • DOI: https://doi.org/10.1007/s10489-016-0773-3

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