基于高斯过程的人体步态关节力矩估计  

Estimation of joint torques in human gait based on Gaussian process

作　　者：李斯绮 张宇玲[1] 杨建涛 LI Siqi;ZHANG Yuling;YANG Jiantao(Institute of Rehabilitation Engineering and Technology,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学康复工程与技术研究所,上海市200093

出　　处：《中国康复理论与实践》2022年第8期989-992,共4页Chinese Journal of Rehabilitation Theory and Practice

摘　　要：目的 提出一种基于高斯过程的下肢关节力矩估计方法,实现人体自然步态下下肢关节力矩精确估计。方法 根据自然步态关节力矩曲线特点,选取平方指数核函数探索关节角度和关节力矩之间的相互关系,建立基于高斯过程的数据融合模型,该模型输入为下肢关节角度,输出为关节力矩。结果 1例健康受试者以0.8 m/s的行走速度在步态跑台行走,采用提出的高斯过程模型进行3次关节力矩预测试验,试验结果显示大部分预测的期望值都落在置信区间内,89%的预测值与实际值的r~2> 0.8。结论 该方法可以较为精确地估计出关节力矩。该研究潜在应用在于优化外骨骼机器人、控制主动式假肢以及调节类人机器人的关节力矩等。Objective To propose an estimation method of lower limb joints torques based on Gaussian process to achieve the accu‐rate estimation of the lower limb joint torques in human gait.Methods According to the characteristics of the natural gait joint torques curve,the squared exponential kernel function was selected to explore the interrelationship between the joint angle and the joint torques.A data fusion model based on Gaussian process was established.The lower limb joint angle was used as the input of the model,and the output was the joint torque.Results One healthy subject walked on the gait running platform with a walking speed of 0.8 m/s.Three joint-torque ex‐periments were conducted using the proposed Gaussian process.The results showed that most of the predicted values fell within the confidence intervals and 89%of the r2 values were greater than 0.8.Conclusion This method could achieve accurate estimation of joint torques.The potential application of this research is to optimize exoskeleton robots,control active prosthesis,and adjust the joint torque of humanoid robots.

关 键 词：高斯过程 人体步态 关节力矩 

分 类 号：R496[医药卫生—康复医学]