The purpose of this study was to examine if peak vertical ground reaction forces during treadmill running can be predicted from kinematic input using machine learning models. Eighteen healthy male runners' hip, knee, and ankle sagittal angles, with subject metadata, were input into random forest, support vector, and multi-layer perceptron regressors. Thirty strides per side at three speeds were pulled for the dataset. Random forest performed the best with a correlation coefficient of 0.950 and a root mean squared error of 0.456, while multi-layer perceptron was the worst with values of 0.948 and 0.462 respectively. The study showed machine learning models can predict peak vertical ground reaction forces.
Dziuk, Cody and Cross, Janelle A.
"PEAK VERTICAL GROUND REACTION FORCE PREDICTION FROM KINEMATICS IN MALE RUNNERS USING MACHINE LEARNING ALGORITHMS,"
ISBS Proceedings Archive: Vol. 41:
1, Article 26.
Available at: https://commons.nmu.edu/isbs/vol41/iss1/26