Correlation Model of High Intensity Volleyball on Exercise-induced Skeletal Muscle Fatigue Based on Nonparametric Random Forest Model

Abstract

In order to study the effect of high intensity volleyball on exercise-induced skeletal muscle
fatigue model, this paper established a one-year mobilization skeletal muscle model of
volleyball. Random forest has fast operation speed and excellent performance in processing big
data. Random forest does not need to worry about the problem of multicollinearity faced by
general regression analysis, and there is no need to choose variables. The established
musculoskeletal model was verified by surface electromyography. Three excellent fefemale
volleyball players were selected as subjects. Nine vicon MCAM2 optical cameras (250 Hz), two
three-dimensional force measuring plates (1000 Hz) and surface electromyography (sEMG)
were used to collect the data synchronously. The data were processed by MATLAB and
anybody software, and the intrinsic dynamic parameters of lower limbs were calculated by the
inverse process of dynamics. The results show that the main muscle groups of volleyball block
take-off action are vastus lateralis, vastus medialis, rectus femoris, tibialis anterior and
gastrocnemius. The trend of sEMG was the same as that of the simulated values: biceps femoris,
medial femoris, lateral femoris, rectus femoris, lateral head of gastrocnemius and medial head of
gastrocnemius. Semitendinosus, semitendinosus and tibialis anterior have different tendency.
The data show that the predicted value is closely related to the sEMG signal by using the
kinematic data to drive the musculoskeletal model, and the established musculoskeletal model is
verified to be in line with the actual muscle activation.