Development of a System to Estimate Muscle Fatigue Level and Provide Feedback Based on Muscle and Soft Tissue Gliding

Abstract

This study aimed to investigate the relationship between changes in fascial glide, ankle dorsiflexion flexibility, jump performance, and subjective fatigue （measured by the Visual Analog Scale; VAS） before and after running. Seventeen collegiate long-distance runners participated in this study. Fascial glide was evaluated by acquiring dynamic ultrasound images of the lower leg and calculating cross-correlation coefficients between the fascia and muscle layers. The differences （Δ values） of each variable before and after treadmill running were analyzed using Lasso regression. Cross-validation determined the optimal regularization parameter （α） as 0.163. Among the predictors, the standardized regression coefficient for Δ fascial glide was the highest （β = 0.271）， indicating the strongest contribution to changes in VAS scores. This result suggests that a decrease in fascial glide is closely associated with an increase in subjective fatigue. The findings provide preliminary evidence that quantifying fascial glide may serve as a useful objective indicator for fatigue assessment. This study highlights the potential importance of fascial mobility as a target for both fatigue monitoring and future AI-based prediction systems.