Movement analysis-based injury prediction model among athletes – a review

Abstract

Background: Movement analysis is a multifaceted field that encompasses various methodologies for studying human motion and behavior across diverse contexts, particularly in sports and rehabilitation. Aims: This review explores the integration of movement screening tools in predicting musculoskeletal injuries. Materials and Methods: The review highlighted the importance of simulation tools, biomechanical analysis, and the significance of machine learning techniques in predicting injuries. The review explored key parameters such as motor control, strength deficits, and movement patterns, the review underscores the potential of predictive models to enhance athlete safety through targeted injury prevention strategies. Results: Despite advancements, challenges remain in the accuracy of injury predictions due to inconsistencies in injury classification and variability among athletes. Conclusions: The review advocates for the development of more refined, sport-specific models that incorporate real-time data analysis and wearable technology, ultimately aiming to bridge the gap in current predictive capabilities and improve athlete health outcomes.