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

Dhruv R Seshadri,Dermot M Phelan,Benjamin J Geletka,Colin K Drummond,Mitchell L Thom,James E Voos,Jeffrey J Hsu,Tim J Gabbett,Ethan R Harlow

doi:10.3389/fspor.2020.630576

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.

Highlights

Sports scientists continually seek newer technologies, data platforms, and therapies to help athletes perform at their highest level while reducing the risk of injury over an arduous season
The collection, agglomeration, and implementation of baseline datasets into analytic models based on data acquired from wearable sensors provide a vast toolkit for team physicians, athletic trainers, and sports scientists to make real-time decisions relevant to the health and wellness of athletes
Utilizing such baseline datasets coupled with analytical models permits sports scientists, data scientists, and medical team personnel to work together to develop efficacious models to track the longterm health of athletes

Summary

Introduction

Sports scientists continually seek newer technologies, data platforms, and therapies to help athletes perform at their highest level while reducing the risk of injury over an arduous season. This review discusses our understanding of how wearable technology and analytics are being used to measure select biomechanical and physiologic parameters in the athlete and the predictive modeling techniques described in the literature to reduce injury burden (Figure 1).

Results

Conclusion