基于小波域隐马尔科夫模型的肌电信号滤波

为了消除肌电信号中的噪声并且保留信号的细节信息,本文提出了基于小波域隐马尔科夫模型的肌电信号消噪方法。该方法利用隐马尔科夫模型对表面肌电信号小波分解后的小波系数之间的相关性进行建模,运用训练模型算法(Expectation-Maximization algorithm,EM算法)估计出该模型的参数,以贝叶斯估计得到真实信号的小波系数,通过重构实现肌电信号的滤波。实验结果表明该方法能有效地去除肌电信号中的噪声,对进一步的特征提取和模式识别创造了良好的条件。     

基于表面肌电的肌肉舒适度评价准则

为了避免操作人员长期处于较差的姿势工作,选取人体上身5个关节进行动作试验,对每个动作变化起主要作用的肌肉块进行了肌电信号测试。通过最大主动收缩法消除不同受试者的肌电信号差异,对肌电百分比值进行归一化和加权平均法处理,根据每个动作变化涉及肌肉的平均生理横断面积,给出复合动作肌肉舒适度指数的计算方法;提出了一套基于表面肌电的肌肉舒适度评价准则,与DELMIA人机仿真软件中的RULA分析结果对比显示,该评价准则具有可行性,适合对操作人员的作业姿势进行评价。     

基于sEMG和GRNN的手部输出力估计

针对智能肌电假手力控制的需要,提出一种基于表面肌电信号(s EMG)和广义回归神经网络(GRNN)的手部输出力估计方法。首先在介绍实验平台的基础上详细描述了肌电信号的采集和特征提取方法以及广义回归神经网络的构建;然后,通过在手臂8个不同部位粘贴肌电传感器来检测手部动作过程中的肌电信号;同时为了全面测量人手在三维空间中的输出力,采用三维力传感器对手部的输出力进行测量;在同步获得手臂上的多通道肌电信号(X)和手部三维力推拉信号(F)后,对采集得到肌电信号进行了特征提取得到特征矩阵X_F;将X_F和F用于构建GRNN网络,并用均方差和残差绝对值均值对手部输出力的估计结果进行评估。为验证该方法的有效性,进行了实验验证,结果表明,该方法能够很好地利用sEMG对手部的输出力进行估计。     

基于表面肌电信号的人体步态事件快速识别方法

针对下肢康复训练机器人主动训练阶段患者运动检测实时性、准确性的需求,提出一种基于动态表面肌电信号的人体步态事件快速识别方法。首先,通过表面肌电信号产生过程数学建模及步态过程中肌肉活动规律分析,给出了基于表面肌电信号强度及其变化特征的步态事件感知原理;其次,以双腿股外侧肌动态表面肌电信号强度及其变化为特征,构建了用于识别支撑和摆动两个步态事件的自适应模糊神经网络模型。实验结果表明：该方法识别结果正确率达95.3%,对足跟触地和脚尖离地事件发生时刻进行识别的平均时间误差分别为21.4ms和24.5ms,同时证明,该方法对步态之间表面肌电信号的差异具有较强的鲁棒性。     

融合脑电-肌电信号的人机协作装配意图识别方法

针对人机协作装配场景下基于单源生理电信号识别协作意图准确率不高、稳定性不好的问题,首先采用支持向量机方法分别从脑电信号和肌电信号识别单源协作装配意图,然后采用D-S证据理论对多源协作装配意图识别结果进行融合,提出了一种融合EEG-EMG信号的人机协作装配意图识别方法。实验结果表明,所提方法可以有效提高人机协作装配意图识别的准确率和稳定性。     

下肢康复机器人人机耦合动力学建模和主动柔顺控制

针对多关节下肢康复机器人非线性人机交互作用力影响训练过程中舒适度和安全性的问题,提出一种人机耦合动力学建模方法和主动柔顺控制策略。结合脑卒中发病初-中期康复训练特点和降低髋关节输出功率的要求,确定出坐卧式下肢康复机器人自由度配置、自平衡结构原理,并完成具体模型设计。基于简化的人体肌骨模型,考虑人机耦合方式,确定出人机交互作用力模型,进一步给出考虑自平衡结构影响的人机耦合动力学模型。引入环境刚度模拟患者踩踏在不同路面的接触力效果,利用动力学前馈进行关节期望力矩补偿控制,并通过李雅普诺夫定理判定控制系统的稳定性。通过实验验证了轨迹跟踪、力跟踪的准确性,进一步通过临床试验,92.2%的脑卒中患者病情得到缓解,验证了研究内容的可行性和安全性。     

利用肌电信号离线控制机械臂

研究的目的是利用人体上肢肌肉的肌电信号来控制机械臂的运动。人体手臂在水平面上做屈伸运动,采集肱二头肌和肱三头肌的肌电信号和肘关节角度信号,对肌电信号进行处理和特征提取,提取的特征值作为一个四层的神经网络模型的输入信号。运用改进后的误差反传学习算法最优化网络各层权值,并使用该神经网络模型来预测人体的肘关节角,使用该预测角来控制机械臂,机械臂的运动与人的肘关节角进行比较,试验结果表明肘关节运动角度与机械臂的运动角度方均根误差小于1°。     

GRNN在肌电预测踝关节运动中的应用

下肢运动预测对于步行康复机器人患者主动训练控制系统的设计具有重要意义.提出一种基于广义回归神经网络(GRNN)的利用肌电信号预测踝关节角轨迹算法:分别用肌电图仪和三维运动捕捉仪同步采集踝关节做屈伸运动时周围五块肌肉的肌电信号和踝关节角度,并对肌电信号进行特征提取.基于主分量分析的数值算法对肌电数据进行降维,得到肌电主分量信号.基于肌电主分量信号利用GRNN算法预测踝关节角轨迹,用黄金分割搜索算法确定GRNN中的最佳平滑参数σ.采用小波消噪算法对踝关节角预测轨迹进行滤波以提高预测精度.用上述算法对9名志愿者进行实验的结果表明:该方法预测精度较高,与BP神经网络预测算法相比运算时间短且预测误差较小,因而更适用于下肢关节角轨迹的在线预测.     

工业机器人抛光作业的主动柔顺控制系统

针对抛光作业过程中的恒力问题,研究了工业机器人恒力抛光作业的主动柔顺控制系统。通过六维力传感器对采集到的力信号进行了滤波、重力补偿,得到抛光工具与工件间的实际接触力;采用模糊PID控制策略,控制恒力补偿装置实现柔顺位置补偿,保证抛光工具与工件间恒定的接触力,从而完成机器人对工件柔顺恒力抛光作业的要求。该方案可以满足抛光机器人对位置和力分别控制的要求。     

带触觉的肌电假手握力模糊控制方法

从注重残疾人本体感出发,提出了一种新的用于肌电假手握力控制的方法.该方法利用短时傅里叶变换思想对信号进行时间片分割,并对时间片内的信号分析选取了几个具有代表性的肌电信号参数,并用训练后的神经网络对其进行分析处理,进而估计出相应的肌肉用力大小.同时将肌电假手指端的力触觉传感器所得的力触觉信号和肌肉用力大小信号输入模糊控制器,根据设计好的模糊控制规则调节肌电假手驱动电机的转动速度从而控制握力大小.该方法可以有效减小个人差异性和肌电信号传感器检测位置不同对控制效果的影响,可以减小肌电传感器安装难度并可供不同的残疾人使用.实验结果证明了该握力控制方法的有效性.     

Comparison of myocontrol and force control based on fitts’ law model

In recent years, many types of input modalities have been developed and used for a great variety of new devices and machines. To enhance the performance of the human-machine systems, well-designed human-machine interface (HMI’s) between the user and the machine are essential. Biosignal-based HMI’s have been appearing as an alternative to physical HMI’s that have been conventionally used. As a type of biosignal control, the electromyography (EMG) has been investigated as an input modality for prostheses, computers, and robotic exoskeletons. In this study, myocontrol is analyzed through direct and numerical comparison with force control. Mycontrol and force control of visual pointing tasks were tested with EMG and force signals provided as visual feedback, and the controllability of each control mode was evaluated based on Fitts’ law paradigm, which is a general estimation method of speed and accuracy of various movements. The experimental results show that both myocontrol and force control can be modeled using Fitts’ law, even when different types of signals were provided as visual feedback. Among the control modes, myocontrol and force control showed high controllability when force signal was used as visual feedback.     

基于肩部协同运动特征的康复外骨骼设计与人机相容性分析

受盂肱关节浮动转动中心的生理运动学属性影响,在基于外骨骼的肩部康复中,人体与外骨骼的兼容性将直接影响康复的效果甚至危及患者的安全。通过分析肩部协同运动特征,提出一种结构紧凑的顺应性肩关节自对准机构,并能够实现独立的锁骨和肩胛骨协同驱动锻炼。基于建立的人机闭链系统,提出4种完整的外骨骼构型,以肩部外骨骼运动空间灵巧性为评价标准,确定了最佳构型,设计并开发肩部外骨骼原型样机。穿戴性能测试试验结果表明,前伸和外摆时人机耦合界面相互作用力从20 N和35 N降低至1 N以下,人体自主外展轨迹和外骨骼穿戴对应点外展轨迹具有良好的吻合性,所提出的顺应性肩部康复外骨骼具有良好的人机相容性。     

外骨骼足端人机接触力测量装置研究

下肢外骨骼机器人系统中,足端接触力的测量是人机交互运动控制的基础,针对设计上面临的足地接触状态复杂、趾关节挠曲运动、重量及尺寸空间限制等问题,提出基于分布式传感器布局,以及刚柔结合的足端人机接触力测量装置结构方案。采用 6 自由度全约束的人机连接方案实现与人足的可靠连接,通过多传感器布置方式来应对复杂的足端受力状况,采用刚柔结合的双层结构,结合多个运动副,解决传感器之间内应力问题,并实现顺应趾关节弯曲的需求,设计了高精度的供电及信号放大电路,并建立力平衡方程实现力信号的合成,最后开展了与三维力台的对比实验,验证了功能指标和测量精度符合设计需求。     

The calculation of a human lumped-mass model from acceleration and force-plate data

This paper describes a technique for the calculation of a lumped-mass representation of a human based on acceleration of body locations, typically obtained from a three-dimensional motion tracking system, and external forces and torques, typically measured from a force plate. The inverse problem of solving for lumped masses is presented, which results in a mass model of the individual subject via a fast, fully automated approach. This method can be used to obtain the mass model per se for the identification of growth deformities or together with a kinematic model for inverse and forward dynamics. Furthermore the mass model and acceleration trajectories subsequently can be used to calculate the contact forces between the floor and the subject at locations remote to a force plate.     

肌电调制结合迭代学习控制的足下垂 FES 系统

足下垂是指由于神经控制功能障碍导致踝关节无法产生背屈以致足尖上抬不完全或不能的现象。功能性电刺激(FES)作为纠正足下垂步态的治疗方法,通过低频脉冲刺激胫骨前肌引起肌肉收缩,使踝关节产生背屈动作,达到矫正足下垂的目的。本文提出了基于肌电(EMG)调制和迭代学习控制(ILC)的FES输出强度调制方法,利用小腿角速度信号通过动态BP神经网络预测健康步态胫骨前肌肌电信号,以脚尖俯仰角作为反馈信号通过ILC输出参考肌电信号,与神经网络预测的肌电信号加权平均得到修正后的肌电信号,最后利用肌肉激活特性调制FES输出。实验表明开环肌电调制模式下的脚尖俯仰角仅有17°左右,而在闭环调制模式下,脚尖俯仰角最大角度达到了21°左右。本文设计的FES控制系统可以帮助足下垂患者进行康复训练。     

Structure Design of Lower Limb Exoskeletons for Gait Training

Due to the close physical interaction between human and machine in process of gait training, lower limb exoskeletons should be safe, comfortable and able to smoothly transfer desired driving force/moments to the patients. Correlatively, in kinematics the exoskeletons are required to be compatible with human lower limbs and thereby to avoid the uncontrollable interactional loads at the human-machine interfaces. Such requirement makes the structure design of exoskeletons very difficult because the human-machine closed chains are complicated. In addition, both the axis misalignments and the kinematic character difference between the exoskeleton and human joints should be taken into account. By analyzing the DOF(degree of freedom) of the whole human-machine closed chain, the human-machine kinematic incompatibility of lower limb exoskeletons is studied. An effective method for the structure design of lower limb exoskeletons, which are kinematically compatible with human lower limb, is proposed. Applying this method, the structure synthesis of the lower limb exoskeletons containing only one-DOF revolute and prismatic joints is investigated; the feasible basic structures of exoskeletons are developed and classified into three different categories. With the consideration of quasi-anthropopathic feature, structural simplicity and wearable comfort of lower limb exoskeletons, a joint replacement and structure comparison based approach to select the ideal structures of lower limb exoskeletons is proposed, by which three optimal exoskeleton structures are obtained. This paper indicates that the human-machine closed chain formed by the exoskeleton and human lower limb should be an even-constrained kinematic system in order to avoid the uncontrollable human-machine interactional loads. The presented method for the structure design of lower limb exoskeletons is universal and simple, and hence can be applied to other kinds of wearable exoskeletons.     

BODY PRESSURE DISTRIBUTION OF AUTOMOBILE DRIVING HUMAN MACHINE CONTACT INTERFACE

Aiming at the fatigue and comfort issues of human-machine contact interface in automo- bile driving and based on physiological and anatomical principle,the physiological and biochemical process of muscles and nerves in the formation and development of fatigue is analyzed systematically. The fatigue-causing physiological characteristic indexes are mapped to biomechanical indexes like muscle stress-strain,the compression deformation of blood vessels and nerves etc.from the perspec- tive of formation mechanism.The geometrical model of skeleton and parenchyma is established by applying CT-scanned body data and MRI images.The general rule of comfort body pressm'e distribu- tion is acquired through the analysis of anatomical structure of buttocks and femoral region.The comprehensive test platform for sitting comfort of 3D adjustable contact interface is constructed.The test of body pressure distribution of human-machine contact interface and its comparison with subjec- tire evaluation indicates that the biomechanical indexes of automobile driving human-machine con- tact interface and body pressure distribution rule studied can effectively evaluate the fatigue and comfort issues of human-machine contact interface and provide theoretical basis for the optimal de- sign of human-machine contact interface.     

具有压力和位置检测功能的气囊型触觉传感器

为了提高机器人与外界环境的安全交互性能,基于导电面电势分布理论和封闭气体压缩定律提出一种适用于机器人曲表面的触觉传感器模型,实现了接触位置与接触压力的检测。传感器采用三层结构,包括导电层、压缩气体隔离层和信号提取层。为了适应任意曲面形状,降低导电层电势非线性分布影响,采用机器学习算法对电势分布模型进行重建。采用COMSOL软件对传感器导电层进行物理建模与仿真,并制备了传感器样品。仿真和实验结果表明,提出的触觉传感器模型可以定制于机器人曲表面上,并能实现接触位置和接触压力的实时检测,可用于人机信息交互。