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The role of technology for accelerated motor learning in sport

Personal and Ubiquitous Computing volume 25, pages 969–978 (2021)Cite this article

Abstract

Wearable devices measuring some physical or physiological quantity of an individual have already become a part of daily life for many people. While such simple devices output mainly the statistical values of measured quantities or count events, demands in sport are more stringent. Quantities of interest must be measured in a wider range, with a greater precision, and with a higher sampling frequency. We present a short introduction to motor learning in sport and its needs for technology back-up. We present properties and limitations of various sensors used for sport activity signal acquisition, means of communication, and properties and limitations of communication channels. We shed some light on the analysis of various aspects of sport activity signal and data processing. We present timing, spatial, and computational power constraints of processing. Attention is given also to the state of the art data processing techniques such as machine learning and data mining. We also put into context some technological trends and challenges in sport, such as the Internet of Things, fog and cloud computing, smart sport equipment, and real-time biofeedback systems and applications.

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Acknowledgments

The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0246).

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Matevž Pustišek, Anton Umek & Anton Kos

  2. Computer Teaching and Research Section, Capital University of Physical Education and Sports, Beijing, China

    Yu Wei

  3. Business school, Beijing Normal University, Beijing, China

    Yunchuan Sun

Authors
  1. Matevž Pustišek
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  2. Yu Wei
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  3. Yunchuan Sun
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  4. Anton Umek
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  5. Anton Kos
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Correspondence to Yu Wei.

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Pustišek, M., Wei, Y., Sun, Y. et al. The role of technology for accelerated motor learning in sport. Pers Ubiquit Comput 25, 969–978 (2021). https://doi.org/10.1007/s00779-019-01274-5

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  • DOI: https://doi.org/10.1007/s00779-019-01274-5

Keywords

  • Motor learning
  • Sensors
  • Sport activity signal acquisition
  • Biofeedback system
  • Sport data analysis
  • Machine learning in sport
  • Smart sport equipment