Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.

膝关节接触力（KCF）是评估膝关节损伤患者膝关节功能的重要因素。然而，由于侵入性，昂贵的价格和时间消耗，基于仪器化的假体植入物或逆动力学分析的KCF测量受到限制。在这项工作中，我们通过结合人工鱼群和随机森林算法提出了一种KCF预测方法。首先，我们训练了一个随机森林，以基于三个膝关节置换患者的数据集，学习步态参数（输入）和接触压力（输出）之间的非线性关系。然后，我们使用改进的人工鱼群算法来优化基于随机森林的KCF预测模型的主要参数。