Machine Learning in Biomechanical Assessments

Key Findings

• Machine learning algorithms are increasingly used to interpret complex biomechanical data, enhancing the accuracy of training assessments.
• New algorithms for data analysis in biomechanical assessments are improving the precision of performance predictions and injury risk evaluations.
• Machine learning algorithms are being integrated into biomechanical assessments, enabling coaches to predict injury risks based on movement patterns and suggest personalized training modifications.

Practical Applications

• Incorporate machine learning tools to analyze biomechanical data, providing more precise and personalized training programs for athletes.
• Adopt advanced data analysis techniques to refine performance predictions and tailor injury prevention strategies for individual athletes.
• Leverage video analysis combined with machine learning algorithms to streamline the assessment process, offering coaches precise insights into athlete biomechanics.

Sources

• https://www.tandfonline.com/journals/tbbe20
• https://isbs.org/images/files/Full_scientific_program_Final.pdf
• https://www.sciencedirect.com/journal/journal-of-biomechanics