Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden
Bibliographic Data
| ID | 5285523 |
|---|---|
| Authors | Dhruv R Seshadri (0000-0002-2426-8795, Case Western Reserve University, corresponding author), Mitchell L Thom (0000-0001-8327-3597, University School), Ethan R Harlow (0000-0001-9995-5701, University Hospitals of Cleveland), Tim J Gabbett (0000-0002-9950-5505, University of Southern Queensland), Benjamin J Geletka (University Hospitals of Cleveland), Jeffrey J Hsu (0000-0002-9971-5916, University of California, Los Angeles), Colin K Drummond (0000-0002-9053-3649, Case Western Reserve University), Dermot M Phelan, Dermot Phelan (0000-0003-3724-2938), James E Voos (0000-0001-5693-0336, University Hospitals of Cleveland) |
| Year | 2021 |
| Volume | 2 |
| Pages | 630576-630576 |
| Publication date | 2021-01-21 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2020.630576 |
| PMID | 33554111 |
| OpenAlex | W3122333372 |
| Language | EN |
| Citations received | 5 |
| References cited | 105 |
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
Analytics · Athletes · Big data · Data mining · Data science · Embedded system · Physical therapy · Wearable computer · Wearable technology · Workload · Cardiovascular Effects of Exercise · Computer Science · Medicine · Sports injuries and prevention · Sports Performance and Training · Sports Science
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Case Report
| Unique citing works | 5 |
|---|---|
| Citations per year | 1 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |