Parameter Determination Considering Individual Differences for Muscle Fatigue Time Estimation Model Based on Surface-ElectroMyoGram

Muscle condition is evaluated primarily based on physical therapy; however, evaluation has not been quantitative. To quantify muscle fatigue, the authors previously derived and defined “muscle fatigue time,” which quantifies muscle fatigue using frequency analysis based on the surface-ElectroMyoGram, of the biceps brachii. The authors also constructed a muscle fatigue time estimation model based on the relationship between muscle fatigue time and muscle load for each participant. However, as model parameters were participant-dependent, versality is insufficient to broadly apply the method to unknown participants. In this study, we attempted to select physical features that influence method parameters and estimate those parameters from selected physical features using multiple regression analysis. Percent body fat and biceps skinfold thickness were selected as physical features, and parameters were determined that yielded data with an error rate of approximately 13%. These results suggest that the variation in model accuracy between individuals can be eliminated using physical features.