Role of Artificial Intelligence in Sports Biomechanics A Comprehensive Analysis
DOI:
https://doi.org/10.68219/j9cxmf43Keywords:
Sports Biomechanics, Sports Performance, Wearable Sensors, Athlete Monitoring, Motion AnalysisAbstract
The emergence of Artificial Intelligence, or AI, technology has revolutionized sports science and human performance research, lending a vastly different dimension to sports biomechanics. Biomechanical assessments are widely accepted as sound scientifically; however, they typically rely on motion capture equipment in either laboratory environments, or by manually analyzing video footage, and relying on human expertise to do so - all of which can be cumbersome, costly, and lacking in ecological validity. The potential for AI technology to provide new methods for biomechanical assessment, optimization of performance, injury prediction, and development of athletes is made evident through the advanced capabilities offered by AI processing techniques like machine learning, deep learning, computer vision, wearables, and analytics. More specifically, AI methods can enable the identification of movement features, modeling of performance, and, analysis of complex patterns of motion through real-time and continuous monitoring of athletes while they train or compete. Some examples of popular AI techniques that have been employed to enable these capabilities include: supervised learning; unsupervised learning; convolutional neural networks; recurrent neural networks; reinforcement learning; and multimodal data fusion. In addition to providing examples of how AI techniques have been used in running, football, basketball, swimming, tennis, and cricket, this paper will also examine the potential for future applications of the latest advancements in AI technologies to improve the performance of athletes. AI-enabled motion tracking technologies, marker-free biomechanical analysis, wearables, injury predictors, and customized training plans have been considered in this document. Ethical issues with AI, interpretability issues, data governance issues, and explainable AI were examined along with the ethical considerations in sports biomechanics be digital twin technology, edge computing, federated learning, and intelligent human-machine cooperation. In conclusion, it is possible to argue that AI assisted biomechanical analyses improves accuracy, increases ecological validity, reduces costs, and allows for individualized optimization. However, there remain important issues surrounding the standardization of the measures, quality of data, fairness in measurement and the implementation of the measures.
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