Objective classification of deep squat and hurdle step scores in functional movement screen: a 3D motion capture and...

Using 3D motion capture, inverse dynamics analysis, and machine learning methods, this study investigates the biomechanical differences across scoring grades of the deep squat (DS) and hurdle step (HS) tasks in the Functional Movement Screen (FMS), and evaluates the feasibility of objectively classifying FMS scores.

The study included 100 healthy adolescents, all of whom completed the DS and HS tasks. For the DS, 100 valid movement trials were obtained; for the HS, which included hurdle steps on both sides, a total of 200 valid unilateral trials were obtained. A 12-camera infrared optical motion capture system was used to simultaneously collect kinematic data, which was combined with ground reaction forces for inverse dynamics analysis to extract features, including joint angles, joint moments, range of motion (ROM), and bilateral differences. The DS data were analyzed at the trial level, while the HS data were aggregated at the subject level after accounting for bilateral correlations. The Kruskal-Wallis H test and Mann-Whitney U test were used to compare differences across scoring grades. Cross-validation was performed based on subject IDs to construct logistic regression, support vector machine (SVM), random forest, and Ordinal Random Forest (OrdinalRF) models.

In the DS task, significant differences were observed across scoring levels for the ankle dorsiflexion angle difference, ROM of pelvic vertical displacement, and trunk flexion ROM. In the HS task, the hip flexion difference and pelvic rotation ROM demonstrated strong discriminatory power across scoring levels. The Random Forest model performed well in the DS task, with an accuracy of 0.910 ± 0.080 and a squared-weighted Kappa of 0.904 ± 0.061; in the HS task, OrdinalRF showed the highest descriptive performance, with an accuracy of 0.805 ± 0.053 and a squared-weighted Kappa of 0.728 ± 0.094. Misclassifications primarily occurred between adjacent rating levels.

The FMS scores for the DS and HS tasks are based on different biomechanical criteria. The combination of 3D motion capture with inverse dynamics analysis and machine learning provides quantitative support for the objectification of FMS scores; however, feature overlap and classification uncertainty still exist between adjacent grades.