Wearable sensors and machine learning for field-based biomechanical load assessment in sports: a review

Abstract

The field-based assessment and management of biomechanical load, such as joint forces, in sports has high significance for effective athletic training and prevention of injuries. Developments in wearable sensors and machine learning have improved assessment methods, as they enable measurements in real training or competition environments outside the laboratory. This review systematically summarizes the current state of research on wearable sensors combined with machine learning for assessing biomechanical load in sports. Searches were conducted in PubMed and SPORTDiscus. A total of 4,546 articles were identified, of which 42 met the eligibility criteria after screening. Data were extracted on participant characteristics, sports and movement tasks, wearable sensors used, machine learning methods, model input and biomechanical output, validation strategies, and key findings. Running was the most frequently studied sport, although nine other sports were also investigated. Artificial neural networks and linear regression were the most commonly applied machine learning methods. Biomechanical load was most frequently assessed using ground reaction force metrics, followed by movement execution metrics and joint moment metrics. Current studies highlight that wearable sensors and machine learning can predict biomechanical loads, especially in the lower extremities during running. Expanding the diversity of sports studied and improving sensor placement on the upper extremities are necessary to broaden the application of wearable sensors and machine learning for athlete monitoring and injury prevention in real-world settings.