Evaluation of the Ability of Machine Learning-Models to Assess Postural Orientation Errors During a Single-Leg Squat

OBJECTIVES: To reach agreement among experts on visual assessments of postural orientation errors (POEs) during the single-leg squat (SLS), and to use expert agreement assessments as ground truth for machine learning (ML) models to evaluate their ability to classify POEs. DESIGN: Methodological study with mixed-methods design. METHODS: POEs of the lower extremity and trunk were assessed from videos and scored as good, fair, or poor. Three experts visually assessed each repetition for each POE independently and then reached agreement. ML models, one for each POE, were trained to assess POEs, using supervised learning on a subset of videos from the agreement assessment (n = 48). The remaining 99 videos were used to compare the prediction of ML models with the agreement scores (criterion validity), using quadratic weighted kappa (Ƙ), Spearman's correlation coefficient (r s ), and accuracy. RESULTS: Machine learning models for the POEs knee medial to foot position (KMFP), femur medial to shank, and femoral valgus showed strong association/substantial agreement with expert agreement scores (r s = 0.566-0.702, Ƙ = 0.58-0.7). Machine learning models for the POEs pelvis and trunk showed moderate association/fair agreement with expert agreement scores (Ƙ = 0.28-0.4, r s = 0.324-0.432), and the POE foot pronation showed no association/agreement (Ƙ = −0.042, r s = −0.05). ML models predicted the expert agreement score in 53% to 78% of the cases. CONCLUSION: Using ML models as a fast and comprehensive assessment of POEs during the SLS shows promising results, the ML models for the POEs KMFP, femur medial to shank, and femoral valgus indicating good validity. Training on larger datasets and/or modifications to some ML models may lead to improvements in model performance. JOSPT Methods 2025;1(1):17-29. Epub 25 November 2024. doi:10.2519/josptmethods.2024.0086