Predicting sports injuries with machine learning technology: Enhancing athletes’ life expectancy through biomechanical analysis
Abstract
This study investigates the application of machine learning technology and biomechanical analysis in predicting sports injuries to enhance athlete life expectancy. The purpose is to explore the relationships between training practices, previous injuries, biomechanical factors, and athlete engagement with injury prevention technologies. Key issues addressed include the gap between awareness and practical application of these technologies, as well as the need for standardized data collection methods. A quantitative research design was employed, utilizing survey questionnaires distributed to 110 athletes to gather data on their training practices, previous injuries, and engagement with technological tools. Descriptive statistics and correlation analyses revealed significant relationships among the variables, highlighting the importance of effective training practices in reducing injury risks. Findings suggest that while athletes recognize the value of biomechanical assessments and machine learning, there is a need for improved engagement with injury prevention technologies. Recommendations include standardizing data collection protocols, enhancing educational initiatives for athletes and coaches, and addressing ethical concerns related to data privacy.
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