Seshadri, Dhruv R. and Thom, Mitchell L. and Harlow, Ethan R. and Gabbett, Tim J. and Geletka, Benjamin J. and Hsu, Jeffrey J. and Drummond, Colin K. and Phelan, Dermot M. and Voos, James E. (2021) Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden. Frontiers in Sport and Active Living, 2:630576. pp. 1-17.
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Text (Published Version)
Seshadri_Gabbett et al_2021_Wearable technology to optimise workload and reduce injury burden_Frontiers.pdf Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
Wearable sensors enable the real-time and non-invasive monitoring of biomechanical, physiological, or biochemical parameters pertinent to the performance of athletes. Sports medicine researchers compile datasets involving a multitude of parameters that can often be time consuming to analyze in order to create value in an expeditious and accurate manner. Machine learning and artificial intelligence models may aid in the clinical decision-making process for sports scientists, team physicians, and athletic trainers in translating the data acquired from wearable sensors to accurately and efficiently make decisions regarding the health, safety, and performance of athletes. This narrative review discusses the application of commercial sensors utilized by sports teams today and the emergence of descriptive analytics to monitor the internal and external workload, hydration status, sleep, cardiovascular health, and return-to-sport status of athletes. This review is written for those who are interested in the application of wearable sensor data and data science to enhance performance and reduce injury burden in athletes of all ages.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Copyright © 2021 Seshadri, Thom, Harlow, Gabbett, Geletka, Hsu, Drummond, Phelan and Voos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Faculty/School / Institute/Centre: | Current - Institute for Resilient Regions - Centre for Health Research (1 Apr 2020 -) |
| Faculty/School / Institute/Centre: | Current - Institute for Resilient Regions - Centre for Health Research (1 Apr 2020 -) |
| Date Deposited: | 08 Dec 2021 04:21 |
| Last Modified: | 15 Dec 2021 03:51 |
| Uncontrolled Keywords: | wearable sensors; artificial intelligence; machine learning; sports medicine; return-to-play; sports cardiology; workload optimization |
| Fields of Research (2008): | 11 Medical and Health Sciences > 1106 Human Movement and Sports Science > 110699 Human Movement and Sports Science not elsewhere classified |
| Fields of Research (2020): | 42 HEALTH SCIENCES > 4207 Sports science and exercise > 420799 Sports science and exercise not elsewhere classified |
| Identification Number or DOI: | https://doi.org/10.3389/fspor.2020.630576 |
| URI: | http://eprints.usq.edu.au/id/eprint/44776 |
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