Abstract

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Title:: Knee joint contact force prediction using artificial fish swarm and random forest algorithm

Author(s):: LU Wei1; ZHU Ye’an2; XU Weiyi2; 1. Department of Rehabilitation Medicine, Jiangxi Provincial People’s Hospital, Nanchang 330006, China; 2. School of Transportation and Logistics, East China Jiaotong University, Nanchang 330013, China

Keywords:: Keywords: artificial fish swarm algorithm; random forest algorithm; knee replacement; contact force prediction

Abstract:: Abstract: A knee joint contact force prediction method combining artificial fish swarm and random forest algorithm is proposed to measure the contact force of the knee joint. Firstly, chaos transformation is used to construct an uniformly distributed population, and the adaptive vision range strategy and adaptive step strategy are introduced to obtain the improved artificial fish swarm algorithm. Then the gait parameters and knee contact force data of all subjects before intervention are divided into training set (70%) and validation set (30%). The training set is trained by random forest algorithm, and the main parameters of random forest model are optimized by improved artificial fish swarm algorithm. The obtained nonlinear relationship between gait parameters and knee joint contact force is velidated by validation set. Finally, the gait parameters and knee contact force of each subject after intervention are used to test the prediction model. The results show that the model has high accuracy in both validation set and test set. The error of the model in validation set indicates that the model can accurately learn the causality between inputs and outputs; while the error of the model in test set indicates that the trained model can precisely generalize the causality to new inputs.