基于改进机器学习算法的步态识别与预测研究EI北大核心CSCD

针对人体下肢不同步态过程的个体差异和行走过程中步幅随机变化等问题,本文提出一种利用运动姿态信号进行步态识别与预测的方法。研究采用基于免疫粒子群算法(IPSO)优化门控循环单元(GRU)网络算法,建立以人体姿态变化数据为输入,以下一阶段姿态变化数据及准确率为输出的网络模型,以期实现对人体姿态变化的预测。本文首先明确概述IPSO优化GRU算法的过程,采集多名受试者分别执行平地行走、蹲起、坐姿腿屈伸等动作的人体姿态变化数据,通过对比分析IPSO优化的循环神经网络(RNN)、长短期记忆网络(LSTM)、GRU网络识别与预测情况,以验证所建模型的有效性。试验结果显示,优化后的算法可较好预测人体姿态变化,其中平地行走和蹲起动作的均方根误差(RMSE)可精确到10^(-3),坐姿腿屈伸的RMSE可精确到10^(-2);各种动作的R^(2)值均可达0.966以上。以上研究结果表明,优化后的算法可应用于实现康复治疗中人体步态运动评价和步态趋势预测、假肢和下肢康复设备设计等研究,对今后提高患者肢体功能、活动水平和生活独立能力的研究提供参考。

Research on gait recognition and prediction based on optimized machine learning algorithm

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking,this paper proposes a method for gait recognition and prediction using motion posture signals.The research adopts an optimized gated recurrent unit(GRU)network algorithm based on immune particle swarm optimization(IPSO)to establish a network model that takes human body posture change data as the input,and the posture change data and accuracy of the next stage as the output,to realize the prediction of human body posture changes.This paper first clearly outlines the process of IPSO’s optimization of the GRU algorithm.It collects human body posture change data of multiple subjects performing flat-land walking,squatting,and sitting leg flexion and extension movements.Then,through comparative analysis of IPSO optimized recurrent neural network(RNN),long short-term memory(LSTM)network,GRU network classification and prediction,the effectiveness of the built model is verified.The test results show that the optimized algorithm can better predict the changes in human posture.Among them,the root mean square error(RMSE)of flat-land walking and squatting can reach the accuracy of 10^-3,and the RMSE of sitting leg flexion and extension can reach the accuracy of 10^-2.The R² value of various actions can reach above 0.966.The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment,as well as in the design of artificial limbs and lower limb rehabilitation equipment,which provide a reference for future research to improve patients’limb function,activity level,and life independence ability.