Mitigating Sports Injury Risks Using Internet of Things and Analytics Approaches
| Autor: | Ashish Gupta, Marisa A. Colston, Gary B. Wilkerson |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
biology
business.industry Athletes Applied psychology 030229 sport sciences Football Predictive analytics biology.organism_classification 03 medical and health sciences Identification (information) 0302 clinical medicine Quality of life (healthcare) Analytics restrict Physiology (medical) Injury prevention 030212 general & internal medicine Safety Risk Reliability and Quality business Psychology human activities |
| Zdroj: | Risk Analysis. 38:1348-1360 |
| ISSN: | 0272-4332 |
| DOI: | 10.1111/risa.12984 |
| Popis: | Sport injuries restrict participation, impose a substantial economic burden, and can have persisting adverse effects on health-related quality of life. The effective use of Internet of Things (IoT), when combined with analytics approaches, can improve player safety through identification of injury risk factors that can be addressed by targeted risk reduction training activities. Use of IoT devices can facilitate highly efficient quantification of relevant functional capabilities prior to sport participation, which could substantially advance the prevailing sport injury management paradigm. This study introduces a framework for using sensor-derived IoT data to supplement other data for objective estimation of each individual college football player's level of injury risk, which is an approach to injury prevention that has not been previously reported. A cohort of 45 NCAA Division I-FCS college players provided data in the form of self-ratings of persisting effects of previous injuries and single-leg postural stability test. Instantaneous change in body mass acceleration (jerk) during the test was quantified by a smartphone accelerometer, with data wirelessly transmitted to a secure cloud server. Injuries sustained from the beginning of practice sessions until the end of the 13-game season were documented, along with the number of games played by each athlete over the course of a 13-game season. Results demonstrate a strong prediction model. Our approach may have strong relevance to the estimation of injury risk for other physically demanding activities. Clearly, there is great potential for improvement of injury prevention initiatives through identification of individual athletes who possess elevated injury risk and targeted interventions. |
| Databáze: | OpenAIRE |
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