基于Hammerstein模型的腕关节功能电刺激控制技术研究

非线性系统的控制通常比较复杂,基于模型辨识的自适应控制方法可以有效地提高控制精度和稳定性。本文根据功能电刺激(FES)下腕关节运动的非线性和时变性特点,利用Hammerstein模型,实现了腕关节运动的FES建模及辨识。通过M序列和逆M序列激励信号,将FES腕关节运动模型分为线性时变和静态非线性两部分。考虑到静态非线性部分的特点,在运动控制过程中只需要根据线性时变特点确定相应的自适应调节策略,将一个复杂非线性问题简化为线性控制问题。对腕关节运动的FES仿真实验也验证了方法的有效性。     

用于麻痹四肢运动功能恢复和训练的功能电刺激系统

功能电刺激(FES)是用于神经肌肉系统的一种康复技术。它可用于对由于脊柱损伤产生的运动中心异常所导致的正常肌肉运动困难的四肢运动功能的康复。FES也可用于功能的改善和训练,例如肌肉萎     

鲁棒PID控制策略在FES脚踏车控制系统中的应用及仿真研究

本研究以英国GJasgow大学康复研究中心所提供的三个脊髓损伤患者的实验数据为基础，通过系统辨识获得了被控对象的数学模型，并将鲁棒PID控制算法应用于功能性电刺激脚踏车系统的速度控制，从而实现了鲁棒PID速度控制器设计目的。仿真结果表明，FES脚踏车系统能实现对任意速度轨迹的跟踪，并且轨迹跟踪精度高、抗干扰能力及鲁棒性较强，该算法为FES脚踏车系统速度控制提供了一种新的鲁棒控制策略。     

用于功能电刺激的手指向系统的开发和评估

由中枢神经系统损伤导致的肢体残疾患者可以通过电刺激肌肉收缩而获得康复。该原理称为功能电刺激(functionalelectricalstimulation,FES)。在日常生活中,拿一件东西是这些患者必要的功能之一。作者开发了一套通过对上肢肌肉进行电刺激,从而使手能够移动到预想目标的系统。受试者戴一顶装有红色指示灯的帽子,手腕上安装一个位置传感器,前臂安装一个只能水平移动的装置(orthosis)。指示灯发出直径约15cm的红色光束,高度约为30cm。光束从受试者头部达到其腕部的传感器,由齿状分布的光电三极管来检测照亮区域的中心。计算机根据位置数据计算…     

用于高位脊髓损伤患者的运动功能康复的多通道FES系统:多接头的上肢康复控制系统

本文论述了一种为肢体瘫痪患者上肢功能康复的多通道机能性电刺激(Function Electrical Stimulation—FES)系统。该系统由一台NEC—PC8801MKⅡ个人计算机、     

功能性电刺激对脊髓损伤的修复作用

目的：回顾功能性电刺激（functional electrical stimulation, FES）对诱导瘫痪或麻痹的肌肉产生功能性或治疗性运动的作用．总结功能性电刺激修复脊髓损伤中应用进展。方法：应用计算机检索CNKI1990年11月至2009年11月有关组织工程修复脊髓损伤方面的文章,检索词”脊髓损伤,电刺激”,限定文献语言种类为中文。同时计算机检索Medline1999年11月至2009年11月有关功能性电刺激在修复脊髓损伤方面的文章,检索词”spinalcordinjury,electricalstimulation”,限定文献语言种类为English。对资料进行初审。选取功能性电刺激修复脊髓损伤方面的相关文献,查找全文,对文献分析,总结研究内容。结果：功能性电刺激通过一定频率电刺激诱导瘫痪或麻痹的肌肉产生功能性或治疗性运动,可以达到改善或恢复被刺激肌肉或肌群功能的目的。功能性电刺激技术可以恢复截瘫患者的部分运动功能。结论：功能性电刺激是现代康复工程领域具有应用前景的一项新的治疗性技术。功能性电刺激技术可以可以达到改善脊髓损伤患者被刺激肌肉或肌群功能,恢复截瘫患者的部分运动功能。     

复合辅助系统——运动神经修复术

当今应用于肢体残疾患者的运动康复方法有两种、它们是功能性的电刺激(Function Electrical System—FES)和机械假肢(Mechanical Bracing)。由于某些因素(如随意性、心理、社会及经济状况、工艺水平等)。这两种辅助系统在应用上受到一定限     

抑制腕部病理性震颤的功能性电刺激系统的仿真与实验

研发一种用于抑制腕部病理性震颤的功能性电刺激（FES）康复系统,包括信息处理、控制器设计、仿真建模以及实验测试。仿真阶段利用Matlab软件构建了腕关节肌肉骨骼模型;设计控制器采用比例-积分-微分（PID）算法,对肌骨模型中存在的震颤扰动进行抑制。仿真结果表明,控制器保证了在抑制高频低幅震颤扰动的同时不影响其低频高幅的自主运动,对震颤扰动幅值的抑制平均达到约95%。实验阶段搭建了FES闭环控制系统实验平台,由电刺激器、角度传感器、电脑控制器和输出板卡以及实时控制软件系统组成。研发的系统在5例健康受试者及1例震颤患者身上进行了测试,震颤抑制效果分别约为90%和80%。     

基于认知再学习疗法的脑卒中康复治疗系统的设计及临床试验

针对脑卒中患者运动功能康复问题,提出认知再学习疗法、肌电视觉反馈疗法和肌电触发神经肌肉电刺激疗法三者结合的运动功能康复复合疗法,开发了一种基于嵌入式技术的脑卒中康复治疗系统以实现复合疗法。以Advanced RISC Machines (ARM) 9处理器S3C2440为控制核心,利用sEMG采集电路实现对脑卒中患者患肢肌电信号实时采集,并通过触摸显示模块实现肌电信号可视化,同时将采集到的信号与所设定的阈值相比较,根据比较的结果决定是否触发NMES刺激电路以实现有效的肌肉电刺激。软件设计运用Linux+QT平台开发,实现肌电参数的处理和显示,以及刺激参数的设置。采用脑卒中康复治疗系统对急性脑卒中患者(n=8)进行上肢运动功能治疗,试验结果表明接受系统康复治疗的试验组FMA上肢评分高于对照组(n=8, P<0.05),肌力分级水平也得到一定的提高。脑卒中康复治疗系统性能稳定,可显著改善脑卒中患者运动功能障碍。     

融合功能性电刺激的助行康复外骨骼机器人混合控制策略研究进展

运动康复有助于促进脊柱损伤的恢复或脑卒中患者神经可塑,这对患者受损运动功能的恢复有着非常重要的作用。近年来,功能性电刺激(FES)技术与外骨骼机器人的结合,发挥了这两种康复技术的优势,同时互补了各自缺陷,从而促进实现了更有效的康复辅助模式,目前已逐渐成为本领域的研究热点。综述融合FES与下肢康复机器人混合控制策略的研究现状,并分别就FES结合被动矫形器的单向控制方法和融合主动外骨骼的协同控制方法,剖析其相关技术与难点;讨论构建人机信息交互环路的关键问题,以及如何设计出合理高效混合控制策略,从而实现动态控制分配和患者最大程度主动参与康复训练的目标。对未来的混合康复技术发展方向进行了总结与展望。     

Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic

This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.     

服务于功能性电刺激的双足步态实验

背景：功能性电刺激利用低频弱电流脉冲刺激失去神经控制的肌肉已经在截瘫行走的临床应用中取得了小范围成功,但现有的电刺激模式存在不灵活、不易操作、且稳定性不高的缺点。 目的：基于步态分析方法,研究涉及到步行动作的各肌肉群的协同动作关系,将肌肉电刺激模式简化为无需患者操作的规律性控制策略,并验证该策略在功能性电刺激实验中的有效性。 方法：针对双足步行的特点,提出一种基于关节角变化趋势及肌电信号强度变化的步态研究方法,旨在服务于功能性电刺激的设计,为下肢肌肉提供理想的电刺激模式,使人体产生相应的肌肉群协同动作,从而使受试者最终实现非自主控制的行走运动。 结果与结论：实验结果一方面验证了基于步态分析的电刺激模式设计是可行的,对今后加入更复杂的控制方式提供了依据,另一方面也为未来开展的瘫痪患者临床康复实验研究打下了基础。     

基于ERD/ERS脑电信号的智能化功能电刺激系统的设计

设计一套基于事件相关同步电位(Event related desynchron ization,ERD)和事件相关去同步电位(Event related synchronization,ERS)脑电信号反馈控制功能电刺激仪系统。当大脑想象残肢运动中,出现ERD/ERS脑电信号,经过特征提取和特征分类转换为控制命令去触发功能电刺激系统,实现对相应残肢的电刺激。实验结果成功实现了对残肢的电刺激。     

Metrological characterization of a cycle-ergometer to optimize the cycling induced by functional electrical stimulation on patients with stroke

Functional electrical stimulation (FES) is a well established method in the rehabilitation of stroke patients. Indeed, a bilateral movement such as cycling induced by FES would be crucial for these patients who had an unilateral motor impairment and had to recover an equivalent use of limbs. The aim of this study was to develop a low-cost metrologically qualified cycle-ergometer, optimized for patients with stroke. A commercial ergometer was instrumented with resistive strain gauges and was able to provide the torque produced at the right and left crank, independently. The developed system was integrated with a stimulator, obtaining a novel FES cycling device able to control in real-time the movement unbalance. A dynamic calibration of the sensors was performed and a total torque uncertainty was computed. The system was tested on a healthy subject and on a stroke patient. Results demonstrated that the proposed sensors could be successfully used during FES cycling sessions where the maximum torque produced is about 9 N m, an order of magnitude less than the torque produced during voluntary cycling. This FES cycling system will assist in future investigations on stroke rehabilitation by means of FES and in new exercise regimes designed specifically for patients with unilateral impairments.     

A decentralized adaptive fuzzy robust strategy for control of upright standing posture in paraplegia using functional electrical stimulation

In this paper, we present a novel decentralized robust methodology for control of quiet upright posture during arm-free paraplegic standing using functional electrical stimulation (FES). Each muscle–joint complex is considered as a subsystem and individual controllers are designed for each one. Each controller operates solely on its associated subsystem, with no exchange of information between them, and the interaction between the subsystems are taken as external disturbances. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle–joint dynamics, a robust control framework is proposed. The method is based on the synergistic combination of an adaptive nonlinear compensator with sliding mode control (SMC). Fuzzy logic system is used to represent unknown system dynamics for implementing SMC and an adaptive updating law is designed for online estimating the system parameters such that the global stability and asymptotic convergence to zero of tracking errors is guaranteed. The proposed controller requires no prior knowledge about the dynamics of system to be controlled and no offline learning phase. The results of experiments on three paraplegic subjects show that the proposed control strategy is able to maintain the vertical standing posture using only FES control of ankle dorsiflexion and plantarflexion without using upper limbs for support and to compensate the effect of external disturbances and muscle fatigue.     

基于中枢模式发生器控制的电刺激步行康复系统设计与实验

研发功能性电刺激(FES)康复系统来实现下肢瘫痪病人的行走运动。控制器采用基于中枢模式发生器(CPG)的仿生控制机理。针对基于递归神经振荡器的CPG模型,研究关键参数(激励性输入、时间常数、感觉反馈、输出阈值)对CPG输出幅值、频率、相位以及占空比的影响。建立包含12个神经元的CPG网络,以控制双腿的4个关节(左/右髋关节和膝关节)和八组肌群(左/右髂腰肌、臀大肌、股直肌和腘绳肌)。所搭建的实验系统和平台,包括悬吊减重系统、助行系统、电刺激系统和运动检测系统。在正常受试者参与的实验中,受试者在CPG控制的电刺激作用下产生非自主行走运动。实验结果表明,受试者双腿的髋关节和膝关节角度与正常人自主行走时的数据达到定性吻合,验证所设计的CPG控制器在FES康复系统中的可行性与有效性。     

Reliability of neural-network functional electrical stimulation gait-control system

Functional electrical stimulation (FES) has been used for restoring walking in spinal-cord injured (SCI) persons. Using artificial intelligence (Al), FES controllers have been developed that allow the automatic phasing of stimulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these Al systems. Neural networks were used to construct FES controllers to control the timing of stimulation. Different numbers of sensors in the sensor set and different numbers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The results show the neural-network controllers can maintain a high accuracy (around 90% for the two- and three-sensor groups and 80% for the onesensor group) over a period of six months. Two or three sensors were sufficient to provide enough information to construct a reliable FES control system, and the number of data points did not have any effect on the reliability of the system.     

Reliability of neural-network functional electrical stimulation gait-control system

Functional electrical stimulation (FES) has been used for restoring walking in spinal-cord injured (SCI) persons. Using artificial intelligence (AI), FES controllers have been developed that allow the automatic phasing of stimulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these AI systems. Neural networks were used to construct FES controllers to control the timing of stimulation. Different numbers of sensors in the sensor set and different numbers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The results show the neural-network controllers can maintain a high accuracy (around 90% for the two- and three-sensor groups and 80% for the one-sensor group) over a period of six months. Two or three sensors were sufficient to provide enough information to construct a reliable FES control system, and the number of data points did not have any effect on the reliability of the system.     

一种新型自适应动态滤波器在超声成像中的应用

目的 为使动态滤波器的中心频率可实时匹配回波信号中心频率,本文设计了一种新型的自适应动态滤波器.方法 本文自适应机制是以快速傅里叶变换（fast Fourier transform,FFT）算法实时分析真实回波信号的频率特性,根据频率编号自适应匹配动态滤波器的系数.该滤波器的所有模块基于现场可编程门阵列（field programmable gate array,FPGA）硬件实现,最后将加入白噪声的正弦信号使用Modelsim时序仿真工具进行仿真.结果 仿真结果表明,这种滤波器能良好匹配回波信号中心频率,消除噪声.结论 本文提出的自适应动态滤波器满足高速实时系统的要求,为超声系统中滤波器的设计提供了可靠依据.     

Efficient FES triggering applying Kalman filter during sensory supported treadmill walking

In this paper an algorithm for a functional electrical stimulation (FES) gait re-education system for incomplete spinal cord injured persons, providing efficient stimulation triggering, is presented. During neurological impaired gait FES was provided as motor augmentation support. Simultaneously the gait kinematics were recorded using the proposed sensory system, which is equipped with a dual-axial accelerometer and a gyroscope. The sensory device was placed at the shank of the paretic leg. The data assessed were input into a mathematical algorithm applied for shank angle estimation. The algorithm is based on the Kalman filter, estimating the angle error and correcting the actual measurement. Furthermore the information was combined with other kinematic data for the purpose of efficient and reliable stimulation triggering. The algorithm was tested with preliminary measurements on several neurologically intact persons during even terrain and treadmill walking. Trial measurements were verified with a contactless optical measurement system, with FES only simulated on controller output. Later on a treadmill training in combination with FES triggering was carried out. The outcome of the measurements shows that the use of sensory integration may successfully solve the problem of data assessment in dynamic movement where an inclinometer does not provide sufficient information for efficient control of FES.