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Smart Health pp 99–124Cite as

Personalized Physical Activity Monitoring Using Wearable Sensors

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8700))

Abstract

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

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Notes

  1. 1.

    The dataset is publicly available at http://www.pamap.org/PAMAP_trials.tar.gz.

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Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence, 67663, Kaiserslautern, Germany

    Gabriele Bleser, Daniel Steffen & Markus Weber

  2. ACTLab, University of Passau, 94032, Passau, Germany

    Attila Reiss

  3. Department of Electrical Engineering, Linköping University, SE-581 83, Linköping, Sweden

    Gustaf Hendeby

  4. Department of Sensor and EW Systems, Swedish Defence Research Agency (FOI), SE-581 11, Linköping, Sweden

    Gustaf Hendeby

  5. Université de Poitiers, 86000, Poitiers, France

    Laetitia Fradet

Authors
  1. Gabriele Bleser
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  2. Daniel Steffen
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  3. Attila Reiss
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  4. Markus Weber
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  5. Gustaf Hendeby
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  6. Laetitia Fradet
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Corresponding author

Correspondence to Gabriele Bleser .

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Editors and Affiliations

  1. Research Unit HCI-KDD, Medical University of Graz, Graz, Austria

    Andreas Holzinger

  2. Industrial HCI Research Lab, Fraunhofer Institute, Lemgo, Germany

    Carsten Röcker

  3. Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany

    Martina Ziefle

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Bleser, G., Steffen, D., Reiss, A., Weber, M., Hendeby, G., Fradet, L. (2015). Personalized Physical Activity Monitoring Using Wearable Sensors. In: Holzinger, A., Röcker, C., Ziefle, M. (eds) Smart Health. Lecture Notes in Computer Science(), vol 8700. Springer, Cham. https://doi.org/10.1007/978-3-319-16226-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-16226-3_5

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  • Print ISBN: 978-3-319-16225-6

  • Online ISBN: 978-3-319-16226-3

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