Forecasting sports-related injuries using wearable devices and data analysis methods - Balnur Kenjayeva; Moldir Kizdarbekova; Mirambek Murzabekov (Retos: Nuevas Tendencias en Educación Física, Deportes y Recreación)
This study investigates the effectiveness of wearable technology in predicting injury rates among athletes, focusing on both micro and severe injuries. Over a 20-week period, 80 physical culture students were divided into an experimental group, using wearable devices for real-time data monitoring, and a control group employing traditional training methods. The study utilized a range of wearable sensors to collect comprehensive physiological and biomechanical data, which was analyzed using custom Python-based tools. Results indicated a significant reduction in micro injuries within the experimental group, affirming the hypothesis that wearable technology can decrease injury incidence through personalized training adjustments. However, the impact on severe injuries was not statistically significant, highlighting the technology's limitations in predicting and preventing acute injuries. This research underscores the potential of wearable devices to enhance athlete safety through data-driven insights but also points to the need for further studies to fully understand and leverage technology in preventing more serious injuries. The findings have important implications for sports science, suggesting a paradigm shift towards more technologically integrated training regimes to optimize health outcomes and performance in athletic populations.