Artificial neural networks' estimations of lower-limb kinetics in sidestepping: Comparison of full-body vs. lower-body landmark sets.

Artificial neural networks (ANNs) offers potential for obtaining kinetics in non-laboratory. This study compared the estimation performance for ground reaction forces (GRF) and lower-limb joint moments during sidestepping between ANNs fed with full-body and lower-body landmarks. 71 male college soccer athletes executed sidestepping while three-dimensional kinematics and kinetics were collected to calculate joint moments by inverse dynamic. To estimate GRF and lower-limb joint moments, coordinates of 18 full-body (the full-body landmarks ANN) and 11 lower-limb body landmarks (the lower-body landmarks ANN) were respectively used as inputs in ANNs. Estimation performance was evaluated using the coefficient of multiple correlations, root mean square error (RMSE), and normalized RMSE (nRMSE) between estimated and measured results. A Wilcoxon signed-rank test determined the difference in estimation performance between the two types of ANNs. Statistical parametric mapping determined the difference between the estimated and measured curves. The lower-body landmarks ANN showed lower error for sagittal knee moments (RMSE: p < 0.001; nRMSE: p < 0.001), but higher error for sagittal hip (RMSE: p = 0.015) and ankle moments (RMSE: p = 0.001; nRMSE: p = 0.001). Significant differences between the lower-body landmarks ANN estimates and measurement curves were found in anterior-posterior GRF (10-12 %, p = 0.013), vertical GRF (5-15 %, p < 0.001), and hip transverse moment (1 %, p = 0.017). No significant differences were found in the estimated and measured GRF peaks. The ANN only using lower-body landmarks as inputs could accurately estimate GRF and lower-limb joint moments during sidestepping, with better performance for knee moments, while ANN using full-body landmarks performs better for hip and ankle moments.