Estimation of Lower Limb Dynamics Parameters for Sidestepping Based on Artificial Neural Networks by the Coordinates of the Whole Body Joint Points
Objective:Applying artificial neural networks(ANNs)with human joint coordinates as input variables to estimate the ground reaction forces(GRF)and lower limb joint torque dur-ing the sidestepping of athletes.Methods:Motion infrared high-speed motion capture system and Kistler three-dimensional force platform(FP)were used to synchronously collect the kine-matics and dynamics data of 71 male football players in the process of completing the sidestep-ping.18 landmarks coordinates on whole-body as inputs in ANNs were used to estimate GRF and lower joint moments.The correlation coefficient,root mean square error(RMSE)and nor-malized root mean square error(nRMSE)were used to evaluate the validity of ANNs estimation.Statistical parameter mapping was used to analyze the difference between the ANNs estimation and the actual curves.Results:There was a good correlation between the kinetics estimated by the ANNs and the measured values(r=0.82-0.97).The errors of the kinetics estimated by the ANNs were significantly different in all directions(P＜0.050).The sagittal plane had a higher correlation coefficient and fewer nRMSE.Only the anterior-posterior(10%—12%,P=0.011;93%—95%,P=0.015)and vertical(5%—15%,P＜0.001)GRF estimated by ANNs were signif-icantly different with measurement curves,as well as the frontal(99%—100%,P=0.015)and transverse(1%,P=0.017)hip moments.There was no significant difference between the peak values of the GRF in the three directions estimated by the ANNs and the measured by FP.Con-clusions:With body landmarks coordinates as inputs,the GRF and lower limb joint moments of soccer players during sidestepping could be effectively estimated by ANNs,especially in the sagittal plane,which could be used in a non-laboratory environment.
万方数据
维普
