Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Dhruv R. Seshadri (Author), Mitchell L. Thom (Author), Ethan R. Harlow (Author), Tim J. Gabbett (Author), Benjamin J. Geletka (Author), Jeffrey J. Hsu (Author), Colin K. Drummond (Author), Dermot M. Phelan (Author), James E. Voos (Author)
Format: Book
Published: Frontiers Media S.A., 2021-01-01T00:00:00Z.
Subjects:
Online Access:Connect to this object online.
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 doaj_8c8a1d3da5d84fb59ff5fcabab4516c0
042 |a dc 
100 1 0 |a Dhruv R. Seshadri  |e author 
700 1 0 |a Mitchell L. Thom  |e author 
700 1 0 |a Ethan R. Harlow  |e author 
700 1 0 |a Ethan R. Harlow  |e author 
700 1 0 |a Tim J. Gabbett  |e author 
700 1 0 |a Tim J. Gabbett  |e author 
700 1 0 |a Benjamin J. Geletka  |e author 
700 1 0 |a Benjamin J. Geletka  |e author 
700 1 0 |a Jeffrey J. Hsu  |e author 
700 1 0 |a Colin K. Drummond  |e author 
700 1 0 |a Dermot M. Phelan  |e author 
700 1 0 |a James E. Voos  |e author 
700 1 0 |a James E. Voos  |e author 
245 0 0 |a Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden 
260 |b Frontiers Media S.A.,   |c 2021-01-01T00:00:00Z. 
500 |a 2624-9367 
500 |a 10.3389/fspor.2020.630576 
520 |a 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. 
546 |a EN 
690 |a wearable sensors 
690 |a artificial intelligence 
690 |a machine learning 
690 |a sports medicine 
690 |a return-to-play 
690 |a sports cardiology 
690 |a Sports 
690 |a GV557-1198.995 
655 7 |a article  |2 local 
786 0 |n Frontiers in Sports and Active Living, Vol 2 (2021) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fspor.2020.630576/full 
787 0 |n https://doaj.org/toc/2624-9367 
856 4 1 |u https://doaj.org/article/8c8a1d3da5d84fb59ff5fcabab4516c0  |z Connect to this object online.