Comprehensive Summary
This literature review showcases the development and validation of machine learning (ML) models for assessing postural orientation errors (POEs) during single-leg squats (SLS), a task commonly used to evaluate lower extremity function and injury risk. The researchers used a systematic approach involving expert visual assessments of SLS videos, categorizing postural errors across various joints into standardized ratings. These labeled evaluations were then used to train ML models using supervised learning techniques. The resulting models showed high accuracy, with strong to substantial agreement for key POEs, particularly knee medial to foot position, femur medial to shank, and femoral valgus, demonstrating ML models potential to enhance clinical assessments.
Outcomes and Implications
The development of machine learning models to automate POE assessments during SLS is highly valuable in clinical settings due to the potential for improving both accuracy and efficiency. Automating this process can significantly reduce the subjective variability and time demands associated with traditional visual assessments by clinicians. Tools such as this could facilitate more frequent and consistent evaluations of patients during rehabilitation, potentially decreasing the risk of future injuries through timely detection and intervention. Although some POEs showed limited validity, ongoing refinement and additional training data could further