1 21 GENERAL INTRODUCTION is associated with the risk of hamstring strain injury in elite Australian footballers,” Br. J. Sports Med., p. bjsports-2016-096777, 2016, doi: 10.1136/bjsports-2016-096777. [17] B. Hulin et al., “Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers,” Artic. Br. J. Sport. Med., 2013, doi: 10.1136/bjsports-2013-092524. [18] B. T. Hulin, T. J. Gabbett, D. W. Lawson, P. Caputi, and J. a Sampson, “The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players,” Br. J. Sports Med., vol. 50, no. 4, pp. 231–236, 2016, doi: 10.1136/bjsports-2015-094817. [19] A. Jaspers, J. P. Kuyvenhoven, F. Staes, W. G. P. Frencken, W. F. Helsen, and M. S. Brink, “Examination of the external and internal load indicators’ association with overuse injuries in professional soccer players,” J. Sci. Med. Sport, vol. 21, no. 6, pp. 579–585, 2018, doi: 10.1016/j.jsams.2017.10.005. [20] V. L. Billat, “Current perspectives on performance improvement in the marathon: From universalisation to training optimisation,” New Stud. Athl., vol. 20:3, pp. 21–39, 2005. [21] A. Rossi, L. Pappalardo, P. Cintia, F. M. Iaia, J. Fernàndez, and D. Medina, Effective injury forecasting in soccer with GPS training data and machine learning, PLoS One, vol. 13, no. 7, pp. 1–15, 2018, doi: 10.1371/journal.pone.0201264. [22] M. Herold, F. Goes, S. Nopp, P. Bauer, C. Thompson, and T. Meyer, “Machine learning in men’s professional football: Current applications and future directions for improving attacking play,” Int. J. Sport. Sci. Coach., vol. 14, no. 6, pp. 798–817, 2019, doi: 10.1177/1747954119879350. [23] B. Caulfield, B. Reginatto, and P. Slevin, “Not all sensors are created equal: a framework for evaluating human performance measurement technologies,” npj Digit. Med., vol. 2, no. 1, 2019, doi: 10.1038/ s41746-019-0082-4. [24] “Industry report on smart wearables market.” https://www.mordorintelligence.com/industry-reports/ smart-wearables-market. [25] R. G. St Fleur, S. M. St George, R. Leite, M. Kobayashi, Y. Agosto, and D. E. Jake-Schoffman, “Use of fitbit devices in physical activity intervention studies across the life course: Narrative review,” JMIR mHealth uHealth, vol. 9, no. 5, 2021, doi: 10.2196/23411. [26] J. Castellano, D. Alvarez-Pastor, and P. S. Bradley, Evaluation of research using computerised tracking systems (amisco® and prozone®) to analyse physical performance in elite soccer: A systematic review, Sport. Med., vol. 44, no. 5, pp. 701–712, 2014, doi: 10.1007/s40279-014-0144-3. [27] S. Ryan, T. Kempton, and A. J. Coutts, “Data reduction approaches to athlete monitoring in professional Australian football,” Int. J. Sports Physiol. Perform., vol. 16, no. 1, pp. 59–65, 2021, doi: 10.1123/ IJSPP.2020-0083. [28] A. Heishman et al., “Associations Between Two Athlete Monitoring Systems Used to Quantify External Training Loads in Basketball Players,” Sports, vol. 8, no. 3, p. 33, 2020, doi: 10.3390/sports8030033. [29] T. Kim, J. H. Cha, and J. C. Park, “Association between in-game performance parameters recorded via global positioning system and sports injuries to the lower extremities in elite female field hockey players,” Cluster Comput., vol. 21, no. 1, pp. 1069–1078, 2016, doi: 10.1007/s10586-016-0690-6. [30] T. Purevsuren, B. Khuyagbaatar, K. Kim, and Y. H. Kim, “Investigation of Knee Joint Forces and Moments during Short-Track Speed Skating Using Wearable Motion Analysis System,” Int. J. Precis. Eng. Manuf., vol. 19, no. 7, pp. 1055–1060, 2018, doi: 10.1007/s12541-018-0125-9.
RkJQdWJsaXNoZXIy MjY0ODMw