Predicting Sports Injuries with Wearable Technology and Data Analysis

被引:42
作者
Zadeh, Amir [1 ]
Taylor, David [2 ]
Bertsos, Margaret [2 ]
Tillman, Timothy [2 ]
Nosoudi, Nasim [3 ]
Bruce, Scott [4 ]
机构
[1] Wright State Univ, Dept Informat Syst & Supply Chain Management, Dayton, OH 45435 USA
[2] Wright State Univ, Dept Kinesiol & Hlth, Dayton, OH 45435 USA
[3] Marshall Univ, Dept Biomed Engn, Huntington, WV USA
[4] Arkansas State Univ, Dept Kinesiol & Hlth, Jonesboro, AR USA
关键词
Sports analytics; Wearable technology; Sports Injuries; Predictive analytics; ROTC; Internet of things (IoTs); OTTAWA ANKLE RULES; LOW-BACK-PAIN; LOWER-EXTREMITY INJURY; RISK-FACTORS; CLASSIFYING PATIENTS; DECISION-MAKING; UNITED-STATES; RUNNING LOADS; SHOULDER PAIN; FOOTBALL;
D O I
10.1007/s10796-020-10018-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Injuries resulting from sports and physical activities can be persistent and pose a substantial problem for player's economic wellbeing and quality of life. Wearable technologies in conjunction with analytics can help mitigate the risk to players by identifying injury risk factors and focusing on risk reduction. Prior to engaging in strenuous sport activities, wearables can be employed to facilitate the quantification of relevant functional capabilities, ultimately advancing the field of sports injury management. In this paper, we discuss how wearable technologies can improve the health and athletic performance of athletes by monitoring participants across many variables. A cohort of 54 army ROTC cadets participated in this study. Using Zephyr BioHarness Wearable technology, we gathered quantifiable data to generate insights that allow us to predict and prevent injuries related the wearer's physical exertion during sporting activities. This study finds that a combination of high BMI and high mechanical loads could result in injury. Therefore, in creating an exercise program, it is imperative to ensure that mechanical load is incrementally increased through the practice season as athletes become conditioned. While, a high level repetitious mechanical load with unconditioned athletes could cause injuries in short time, it is important to impose enough mechanical loads in the training program to ensure good musculoskeletal development. While our analyses identified several factors associated with injury data during ROTC activities, other wearable variables might become significant in other situations. In summary, results from this study demonstrate that wearable technology allows players with an increased risk of injury to be identified and targeted for intervention.
引用
收藏
页码:1023 / 1037
页数:15
相关论文
共 88 条
[11]  
Bruce S.L., 2016, Journal of Athletic Training, V11, P194, DOI DOI 10.4085/1104194
[12]   Clinical Prediction Rules, Part 1: Conceptual Overview [J].
Bruce, Scott L. ;
Wilkerson, Gary B. .
ATHLETIC THERAPY TODAY, 2010, 15 (02) :4-9
[13]   Clinical Prediction Rules, Part 2: Data Analysis Procedures and Clinical Application of Results [J].
Bruce, Scott L. ;
Wilkerson, Gary B. .
ATHLETIC THERAPY TODAY, 2010, 15 (02) :10-13
[14]  
Caparrós T, 2018, J SPORT SCI MED, V17, P289
[15]   The Use of Wearable Microsensors to Quantify Sport-Specific Movements [J].
Chambers, Ryan ;
Gabbett, Tim J. ;
Cole, Michael H. ;
Beard, Adam .
SPORTS MEDICINE, 2015, 45 (07) :1065-1081
[16]   Development and application of clinical prediction rules to improve decision making in physical therapist practice [J].
Childs, JD ;
Cleland, JA .
PHYSICAL THERAPY, 2006, 86 (01) :122-131
[17]   A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: A validation study [J].
Childs, JD ;
Fritz, JM ;
Flynn, TW ;
Irrgang, JJ ;
Johnson, KK ;
Majkowski, GR ;
Delitto, A .
ANNALS OF INTERNAL MEDICINE, 2004, 141 (12) :920-928
[18]  
Chimera NJ, 2017, INT J SPORTS PHYS TH, V12, P173
[19]  
Clark JF., 2015, Optometry Visual Performance, V3, P10
[20]  
Clark JF., 2015, Optometry and Visual Performance, V3, P106