Key result
Automated Functional Movement Screening outperforms traditional models, achieving ~80% accuracy with high expert agreement.
Why the study?
Traditional Functional Movement Screening requires on-site expert assessment, which is time-consuming and subject to bias, making automated screening methods increasingly important.
Does an improved Gaussian Mixture Model improve scoring accuracy for automated Functional Movement Screening compared to traditional models?
Does an improved Gaussian Mixture Model improve scoring accuracy for automated Functional Movement Screening compared to traditional models?
An improved Gaussian Mixture Model using depth camera data provides accurate automated Functional Movement Screening with high agreement to expert scoring.
May support objective Functional Movement Screening in practice; leaves open prospective clinical validation.
Background: Functional Movement Screening (FMS) allows for rapid assessment of an individual’s physical activity level and timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on the improved Gaussian Mixture Model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors, then we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately, finally, we conducted FMS assessment by the Maximum Likelihood estimation. Results: The improved GMM has a higher scoring accuracy (Improved GMM:0.8) compared to other models (Traditional GMM=0.38, Adaboost.M1=0.7, Naïve-Bayes=0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa=0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task and it is potentially feasible to use depth cameras for FMS assessment.
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Hong et al. (2023) studied Functional Movement Screening assessment. Improved Gaussian Mixture Model (GMM) vs. Traditional GMM, Adaboost.M1, Naïve-Bayes, and expert scoring was evaluated on Scoring accuracy and agreement with expert scoring (kappa 0.67). An automated Functional Movement Screening assessment using an improved Gaussian Mixture Model achieved a scoring accuracy of 0.80, outperforming traditional models, with high expert agreement (kappa=0.67).
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