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A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity

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Book cover Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

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Abstract

Physical Activity is important for maintaining healthy lifestyles. Recommendations for physical activity levels are issued by most governments as part of public health measures. As such, reliable measurement of physical activity for regulatory purposes is vital. This has lead research to explore standards for achieving this using wearable technology and artificial neural networks that produce classifications for specific physical activity events. Applied from a very early age, the ubiquitous capture of physical activity data using mobile and wearable technology may help us to understand how we can combat childhood obesity and the impact that this has in later life. A supervised machine learning approach is adopted in this paper that utilizes data obtained from accelerometer sensors worn by children in free-living environments. The paper presents a set of activities and features suitable for measuring physical activity and evaluates the use of a Multilayer Perceptron neural network to classify physical activities by activity type. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 96 % with 95 % for sensitivity, 99 % for specificity and a kappa value of 94 % when three and four feature combinations were used.

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Notes

  1. 1.

    Source: McKinsey Global Institute (2014).

  2. 2.

    http://www.who.int/.

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Authors and Affiliations

  1. Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    P. Fergus, A. Hussain & L. Boddy

  2. Edge Hill University, St. Helens Road, Ormskirk, Lancashire, L39 4QP, UK

    J. Hearty, S. Fairclough & N. D. Ridgers

  3. Swansea University, Singleton Park, Swansea, SA2 8PP, UK

    K. A. Mackintosh & G. Stratton

  4. Deakin University, Geelong, Victoria, Australia

    P. Fergus, A. Hussain, J. Hearty, S. Fairclough, L. Boddy, K. A. Mackintosh, G. Stratton, N. D. Ridgers & Naeem Radi

  5. Al-Khawarizmi International College, Abu Dhabi, UAE

    Naeem Radi

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  1. P. Fergus
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  2. A. Hussain
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  4. S. Fairclough
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  6. K. A. Mackintosh
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  7. G. Stratton
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Correspondence to P. Fergus .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Fergus, P. et al. (2015). A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_67

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_67

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  • Online ISBN: 978-3-319-22186-1

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