上肢康复训练机器人的肌电控制研究
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摘要
人体神经肌肉系统的电活动可以反映肌肉运动状态,并可通过皮肤表面处的sEMG信号测量获取。由于sEMG信号采集方便、对身体无创伤,利用多通道sEMG信号实现基于模式识别的上肢康复训练机器人肌电控制成为当前康复工程领域的研究热点。基于模式识别的肌电控制原理是从多通道sEMG信号中提取能表征不同上肢动作模式的信号特征,通过模式分类器识别出目标动作模式,进而实现上肢康复训练机器人的运动控制。对于基于模式识别的肌电控制研究,评价指标主要包括识别准确率和计算速度。
本文的研究目标是完善基于模式识别的上肢康复训练机器人肌电控制的实现技术,通过研制的上肢康复训练机器人和多通道sEMG信号采集系统,在以下几个方面进行了研究：(1)多通道sEMG信号的空间解耦；(2)表征不同上肢动作模式的sEMG信号特征提取；(3)上肢动作模式分类。
根据sEMG信号微弱、易受干扰的特点,针对性地设计了面向上肢康复训练的sEMG信号采集方案,以降低sEMG信号的随机性,获得一致性好的sEMG信号。本研究中,设定受试者右侧上肢为机器人辅助康复训练的偏瘫侧,左侧为自主引导运动的健康侧,通过分析上肢运动与相关肌肉对应关系,确定了受试者左侧上肢的肱桡肌、肱二头肌、肱三头肌、三角肌中部为四通道sEMG信号的采集位置,测量电极沿肌纤维方向放置在肌腹处,参考电极放置在sEMG信号比较微弱的肌腱处。
研究了基于ICA的多通道sEMG信号空间解耦,以消除四通道sEMG信号采集过程中电极串扰引起的空间耦合冗余信息。利用FastICA算法将四通道sEMG信号分解成四个不同的独立分量,针对多次ICA分解得到的独立分量排列次序不固定的问题,提出了独立分量次序与多通道sEMG信号的匹配方法,以ICA分解前后信号的相关性为依据建立了独立分量和四通道sEMG信号间的一一对应关系,保证了同一动作模式下独立分量的一致性,使得独立分量能取代原始的四通道sEMG信号而成为肌电控制源。特征提取结果表明,空间解耦虽然使得特征空间的可分性略有下降,但大幅提高了特征提取速度,减小了计算量,验证了ICA用于多通道sEMG信号空间解耦的有效性。
研究中提取样本熵作为表征不同上肢动作模式的sEMG信号特征,针对样本熵用于特征提取时计算量大的问题,研究了小波包变换与样本熵相结合的sEMG信号特征提取方法,提取五层小波包分解后九个低频子空间的样本熵作为sEMG信号特征。针对特征空间维数大的问题,提出了一种改进小波包分解算法,在小波变换的基础上对某一高频子空间进行二次小波变换,提取改进小波包分解后四个低频子空间的样本熵作为sEMG信号特征,并与积分肌电值联合构造多域特征,即TDISaEn。基于CSI的特征可分性评价结果与BP神经网络测试结果表明,与常用的以小波包能量构造的特征(TDIEner)相比,TDISaEn特征的可分性要好,六种上肢动作模式的识别准确率也高,证明了特征提取算法的有效性。
针对BP神经网络用于上肢动作模式分类时收敛速度慢、训练时间长的问题,提出了基于遗传BP神经网络的上肢动作模式分类方法,利用遗传算法优化BP神经网络的连接权值和阈值,将遗传迭代后得到的最优解用于训练BP神经网络。结果表明,经遗传算法优化后,虽然BP神经网络的识别准确率提高不大,但是网络训练时间大幅缩短,说明遗传算法对BP神经网络的优化是有效的。
利用自行研制的上肢康复训练机器人构建了基于模式识别的肌电控制原型系统,将训练好的遗传BP神经网络模型集成到机器人控制系统中,用以生成机器人运动控制指令。测试结果表明,原型系统运行可靠,验证了基于模式识别的肌电控制的有效性。
本文将sEMG信号分析处理与上肢康复训练机器人相结合,研究结果有助于提高上肢康复训练机器人性能。本文研究还存在一定的不足,主要有以下两个问题需要继续研究和完善：
(1)肢体动作的不同幅度、速度对sEMG信号的影响规律：(2)受试者样本选
取范围需要扩大,应考虑年龄、性别和偏瘫程度等临床因素。
The sEMG signal is the comprehensive overlay of the electrical activity in the nervous system at the skin surface in space and time. As it is easy to acquire and has no trauma to the body, it is a research focus to realize the upper limb rehabilitation robot myoelectric control utilizing sEMG signal in the field of rehabilitation engineering. The principle of myoelectric control based on pattern recognition is to extract the features that can characterize different motion patterns from muti-channel sEMG signals, and decode the features to the corresponding robot motion control instructions which can drive the upper limb rehabilitation robot and then put the hemiplegic side upper limb into training through the feature pattern classifier. The evaluation indexes of myoelectric control mainly include recognition accuracy and decoding speed.
In this paper, the myoelectric control of upper limb rehabilitation robot based on pattern recognition is studied in order to drive the upper limb rehabilitation robot quickly and accurately utilizing the four channel sEMG signals of healthy side upper limb of hemiplegia patients. Problems need to be studied including:(1) the spatial decoupling pretreatment of multi-channel sEMG signals;(2) the features extraction of sEMG signals to characterize different upper limb movement patterns;(3) the classification of upper limb movement patterns.
According to sEMG's weak and vulnerable to interference characters, the paper specifically designed the sEMG acquisition scheme in order to obtain the reliable sEMG signals. On the basis of analyzing the correspondence relation between upper limb movement and muscle, the four-channel sEMG signals acquisition position were placed on the central of the brachioradialis muscle, biceps, triceps and deltoid on subject's left upper limb, and we also placed the measuring electrodes along the direction of fibers on the muscle belly and reference electrodes on the tendons where the sEMG signals are faint, which reduced the randomness of the signal acquisition and provided the steady control source for the myoelectric control of upper limb rehabilitation robot.
In order to eliminate the spatial coupling redundant information among the four-channel sEMG signals, the paper proposed the multi-channel sEMG signals spatial decoupling pretreatment method based on independent component analysis (ICA), and divided the four-channel sEMG signals into four different independent components. For the shortcoming that the order of the independent component after multiple ICA decomposition is not fixed, the paper proposed the matching method between the order of the independent component and the multi-channel sEMG signals and established the clear corresponding relation between the independent component and four-channel sEMG signals based on the correlation before and after ICA decomposition, which made it possible for the independent component to replace the original sEMG signals to become the myoelectric control source. The subsequent result of feature extraction shows that the spatial decoupling makes the feature space theoretically separability decreased slightly, but it significantly increased the speed of feature extraction and reduced the amount of calculation, which shows it is effective for the ICA multi-channel sEMG signals spatial decoupling pretreatment.
The sample entropy can be used as a characterization of differernt upper limb patterns of sEMG signal feature. But it involves a large amount of calculation. For this limitation, the method by combining wavelet packet transform with sample entropy was studied to extract the sEMG signal feature in this paper. Extract sample entropy from nine subspaces after five layers wavelet packet decomposition as the features of the motion pattern. For the issue that the feature space dimension is too large, an improved wavelet packet decomposition algorithm was proposed, which made a high-frequency subspace quadratic wavelet transform based on the main wavelet transform, and then extracted sample entropy from four sub-spaces features and constructed joint multi-domain features with integrated EMG values that is TDISaEn. Compared to the feature constructed by the common wavelet packet energy (TDIEner), the results based on the CSI and BP neural network test results show that TDISaEn has a higher separability and recognition accruracy than TDIEner.
The network convergence speed is slow and the training time is long using the BP neural network to classify the upper limb motion pattern. In order to overcome this defect, the genetic BP neural network was proposed, which optimize the BP neural network's connection weights and thresholds with genetic algorithm. Then, get the optimal solution after inheritance for training the BP neural network. The results show that after genetic algorithm optimization, the BP neural network recognition accuracy is not high, but the training time was greatly shortened.
The myoelectric control based on pattern recognition was tested utilizing the upper limb rehabilitation robot self-developed. Then integrate the trained genetic BP neural network model into the robot control system and transformed the identified the target movment pattern into the robot motion control commands. The result shows that the upper limb rehabilitation robot performed well and verified the accuracy and stability of myoelectric control.
The paper combines sEMG processing and upper limb rehabilitation robot, and the research results are helpful to improve the performance of the upper limb rehabilitation robot. However, there are still some inadequate, and two following problems need to research and improve:
(1) The related laws between movement amplitude, speed and sEMG signal;(2) The selection range of subjects sample needs to be expanded, and some clinical factors such as age, gender, degree of hemiplegia should be considered.
本文的研究目标是完善基于模式识别的上肢康复训练机器人肌电控制的实现技术,通过研制的上肢康复训练机器人和多通道sEMG信号采集系统,在以下几个方面进行了研究：(1)多通道sEMG信号的空间解耦；(2)表征不同上肢动作模式的sEMG信号特征提取；(3)上肢动作模式分类。
根据sEMG信号微弱、易受干扰的特点,针对性地设计了面向上肢康复训练的sEMG信号采集方案,以降低sEMG信号的随机性,获得一致性好的sEMG信号。本研究中,设定受试者右侧上肢为机器人辅助康复训练的偏瘫侧,左侧为自主引导运动的健康侧,通过分析上肢运动与相关肌肉对应关系,确定了受试者左侧上肢的肱桡肌、肱二头肌、肱三头肌、三角肌中部为四通道sEMG信号的采集位置,测量电极沿肌纤维方向放置在肌腹处,参考电极放置在sEMG信号比较微弱的肌腱处。
研究了基于ICA的多通道sEMG信号空间解耦,以消除四通道sEMG信号采集过程中电极串扰引起的空间耦合冗余信息。利用FastICA算法将四通道sEMG信号分解成四个不同的独立分量,针对多次ICA分解得到的独立分量排列次序不固定的问题,提出了独立分量次序与多通道sEMG信号的匹配方法,以ICA分解前后信号的相关性为依据建立了独立分量和四通道sEMG信号间的一一对应关系,保证了同一动作模式下独立分量的一致性,使得独立分量能取代原始的四通道sEMG信号而成为肌电控制源。特征提取结果表明,空间解耦虽然使得特征空间的可分性略有下降,但大幅提高了特征提取速度,减小了计算量,验证了ICA用于多通道sEMG信号空间解耦的有效性。
研究中提取样本熵作为表征不同上肢动作模式的sEMG信号特征,针对样本熵用于特征提取时计算量大的问题,研究了小波包变换与样本熵相结合的sEMG信号特征提取方法,提取五层小波包分解后九个低频子空间的样本熵作为sEMG信号特征。针对特征空间维数大的问题,提出了一种改进小波包分解算法,在小波变换的基础上对某一高频子空间进行二次小波变换,提取改进小波包分解后四个低频子空间的样本熵作为sEMG信号特征,并与积分肌电值联合构造多域特征,即TDISaEn。基于CSI的特征可分性评价结果与BP神经网络测试结果表明,与常用的以小波包能量构造的特征(TDIEner)相比,TDISaEn特征的可分性要好,六种上肢动作模式的识别准确率也高,证明了特征提取算法的有效性。
针对BP神经网络用于上肢动作模式分类时收敛速度慢、训练时间长的问题,提出了基于遗传BP神经网络的上肢动作模式分类方法,利用遗传算法优化BP神经网络的连接权值和阈值,将遗传迭代后得到的最优解用于训练BP神经网络。结果表明,经遗传算法优化后,虽然BP神经网络的识别准确率提高不大,但是网络训练时间大幅缩短,说明遗传算法对BP神经网络的优化是有效的。
利用自行研制的上肢康复训练机器人构建了基于模式识别的肌电控制原型系统,将训练好的遗传BP神经网络模型集成到机器人控制系统中,用以生成机器人运动控制指令。测试结果表明,原型系统运行可靠,验证了基于模式识别的肌电控制的有效性。
本文将sEMG信号分析处理与上肢康复训练机器人相结合,研究结果有助于提高上肢康复训练机器人性能。本文研究还存在一定的不足,主要有以下两个问题需要继续研究和完善：
(1)肢体动作的不同幅度、速度对sEMG信号的影响规律：(2)受试者样本选
取范围需要扩大,应考虑年龄、性别和偏瘫程度等临床因素。
The sEMG signal is the comprehensive overlay of the electrical activity in the nervous system at the skin surface in space and time. As it is easy to acquire and has no trauma to the body, it is a research focus to realize the upper limb rehabilitation robot myoelectric control utilizing sEMG signal in the field of rehabilitation engineering. The principle of myoelectric control based on pattern recognition is to extract the features that can characterize different motion patterns from muti-channel sEMG signals, and decode the features to the corresponding robot motion control instructions which can drive the upper limb rehabilitation robot and then put the hemiplegic side upper limb into training through the feature pattern classifier. The evaluation indexes of myoelectric control mainly include recognition accuracy and decoding speed.
In this paper, the myoelectric control of upper limb rehabilitation robot based on pattern recognition is studied in order to drive the upper limb rehabilitation robot quickly and accurately utilizing the four channel sEMG signals of healthy side upper limb of hemiplegia patients. Problems need to be studied including:(1) the spatial decoupling pretreatment of multi-channel sEMG signals;(2) the features extraction of sEMG signals to characterize different upper limb movement patterns;(3) the classification of upper limb movement patterns.
According to sEMG's weak and vulnerable to interference characters, the paper specifically designed the sEMG acquisition scheme in order to obtain the reliable sEMG signals. On the basis of analyzing the correspondence relation between upper limb movement and muscle, the four-channel sEMG signals acquisition position were placed on the central of the brachioradialis muscle, biceps, triceps and deltoid on subject's left upper limb, and we also placed the measuring electrodes along the direction of fibers on the muscle belly and reference electrodes on the tendons where the sEMG signals are faint, which reduced the randomness of the signal acquisition and provided the steady control source for the myoelectric control of upper limb rehabilitation robot.
In order to eliminate the spatial coupling redundant information among the four-channel sEMG signals, the paper proposed the multi-channel sEMG signals spatial decoupling pretreatment method based on independent component analysis (ICA), and divided the four-channel sEMG signals into four different independent components. For the shortcoming that the order of the independent component after multiple ICA decomposition is not fixed, the paper proposed the matching method between the order of the independent component and the multi-channel sEMG signals and established the clear corresponding relation between the independent component and four-channel sEMG signals based on the correlation before and after ICA decomposition, which made it possible for the independent component to replace the original sEMG signals to become the myoelectric control source. The subsequent result of feature extraction shows that the spatial decoupling makes the feature space theoretically separability decreased slightly, but it significantly increased the speed of feature extraction and reduced the amount of calculation, which shows it is effective for the ICA multi-channel sEMG signals spatial decoupling pretreatment.
The sample entropy can be used as a characterization of differernt upper limb patterns of sEMG signal feature. But it involves a large amount of calculation. For this limitation, the method by combining wavelet packet transform with sample entropy was studied to extract the sEMG signal feature in this paper. Extract sample entropy from nine subspaces after five layers wavelet packet decomposition as the features of the motion pattern. For the issue that the feature space dimension is too large, an improved wavelet packet decomposition algorithm was proposed, which made a high-frequency subspace quadratic wavelet transform based on the main wavelet transform, and then extracted sample entropy from four sub-spaces features and constructed joint multi-domain features with integrated EMG values that is TDISaEn. Compared to the feature constructed by the common wavelet packet energy (TDIEner), the results based on the CSI and BP neural network test results show that TDISaEn has a higher separability and recognition accruracy than TDIEner.
The network convergence speed is slow and the training time is long using the BP neural network to classify the upper limb motion pattern. In order to overcome this defect, the genetic BP neural network was proposed, which optimize the BP neural network's connection weights and thresholds with genetic algorithm. Then, get the optimal solution after inheritance for training the BP neural network. The results show that after genetic algorithm optimization, the BP neural network recognition accuracy is not high, but the training time was greatly shortened.
The myoelectric control based on pattern recognition was tested utilizing the upper limb rehabilitation robot self-developed. Then integrate the trained genetic BP neural network model into the robot control system and transformed the identified the target movment pattern into the robot motion control commands. The result shows that the upper limb rehabilitation robot performed well and verified the accuracy and stability of myoelectric control.
The paper combines sEMG processing and upper limb rehabilitation robot, and the research results are helpful to improve the performance of the upper limb rehabilitation robot. However, there are still some inadequate, and two following problems need to research and improve:
(1) The related laws between movement amplitude, speed and sEMG signal;(2) The selection range of subjects sample needs to be expanded, and some clinical factors such as age, gender, degree of hemiplegia should be considered.
引文
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