摘要
关节力矩预测在康复医学、临床医学和运动训练等领域有着重要作用,对力矩连续、实时地预测可以使人机交互设备更好地反馈、复刻人体运动意图。为了给患者提供一个安全、主动、舒适的康复训练环境,提升人机交互设备的柔顺性,提出了一种改进型递归小脑模型神经网络模型关节力矩预测方法。该方法采用肌肉协同分析对采集的相关肌肉的表面肌电信号(sEMG)进行降维,将降维后的sEMG特征向量与关节角速度、关节角度作为输入信号,并在小脑模型神经网络中加入递归单元和模糊逻辑规则,以小波函数作为隶属度函数,对非疲劳、过渡疲劳及疲劳这3种状态下的踝关节背屈跖屈运动的动态力矩进行连续预测。力矩预测值与实际值之间的平均皮尔逊相关系数和平均标准均方根误差分别为0.9335和0.1598,实验结果验证了该方法对下肢关节力矩连续预测的准确性和有效性。
The joint torque prediction plays an important role in rehabilitation medicine,clinical medicine,sports training and other fields.The continuous and real-time torque prediction can make the human-computer interaction equipment better feedback and reproduce the intention of human motion.To provide a safe,active and comfortable rehabilitation training environment for patients and enhance the compliance of the human-computer interaction equipment,a novel method of joint torque prediction is proposed,which is based on an improved recursive cerebellar model neural network.In this method,muscle synergy analysis is used to reduce the dimensionality of surface electromyographic(sEMG)signals.Then,the reduced-dimension sEMG feature vector,joint angular velocity and joint angle are used as the input data of the prediction model.In addition,recursive unit and fuzzy logic rules are introduced into the cerebellar model neural network,while the wavelet function is used as membership function.Hence,the generalization ability of the network is optimized.The RWFCMNN model realizes the time series prediction of the dynamic torque of ankle dorsiflexion and plantarflexion in three states,non-fatigue,transitional fatigue and fatigue.The average Pearson correlation coefficient and the average normalized root mean square error between the predicted torque and the actual torque are 0.9335 and 0.1598,respectively.These numerical values verify the accuracy and effectiveness of this method for continuous prediction of lower limb joint torque.
作者
姜海燕
李竹韵
陈艳
Jiang Haiyan;Li Zhuyun;Chen Yan(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology,Fuzhou 350108,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2022年第11期172-180,共9页
Chinese Journal of Scientific Instrument
基金
福建省对外合作项目(2019I1009)资助。
关键词
关节力矩预测
表面肌电信号
小脑模型神经网络
肌肉协同分析
joint torque prediction
surface electromyography
cerebellar model neural network
muscle synergy analysis
作者简介
通信作者:姜海燕,1998年于山东科技大学获得学士学位,2005年于福州大学获得硕士学位,2013年于福州大学获得博士学位,现为福州大学副教授,主要研究方向为生物医学信号检测与处理E-mail:jianghaiyan@fzu.edu.cn;李竹韵,2020年于沈阳建筑大学获得学士学位,现为福州大学硕士研究生,主要研究方向为智能检测与信号处理。E-mail:2207607708@qq.com;陈艳,2018年于厦门工学院获得学士学位,2021年于福州大学获得硕士学位,主要研究方向为生物医学检测与信号处理。E-mail:448046833@qq.com。
