Implementation of a Decision Support System to Enhance Movement Proficiency Assessment in Sport.

Background/

Objectives

This study aimed to determine the relationships between seven descriptors of movement proficiency used by an expert to grade an athlete's single-leg squat and the overall subjective 'grade' and the ability to predict a 'grade' based on the descriptors. A secondary aim was to determine the relationships between biomechanical data, the expert-defined descriptors, and the subjective 'grade' and its ability to predict the descriptors' presence and the overall 'grade'.

Methods

Single-leg squats in 55 male athletes were graded using expert evaluation, synchronized video, biomechanical data, and decision tree and logistic regression analysis.

Results

The model that most accurately predicted 'grade' (94.7%) was a decision tree with the descriptors as inputs. The model with biomechanical data for the descriptor 'foot' was the most accurate one (96.3%), followed by 'lumbar' and 'depth' (85.2%), 'knee' (81.2%), 'pelvis/hip' (71.7%), and 'trunk' (62.3%). These accuracies followed similar order to the intra-rater agreement: 'foot' (0.789), 'lumbar' (0.776), 'knee' (0.725), 'depth' (0.682), 'pelvis/hip' (0.662), and 'trunk' (0.637), indicating that 'trunk', 'pelvis/hip', and 'depth' are potentially the hardest descriptors to assess by the expert.

Conclusions

The models developed in this study demonstrate that subjective perceptions can be somewhat accurately explained through a small number of biomechanical indicators. The results of this study support the notion that human movement evaluations should consider both subjective and objective assessments in a complementary manner to accurately evaluate an athlete's movement proficiency.