仿人型假手多运动模式的肌电控制研究
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摘要
肌电假手是一种面向截肢患者康复,由人体肌肉电信号(Electromyography,EMG)进行控制的仿人型机器手。它是集机械电子、计算机与生物等多学科领域的集成化系统,具有多自由度、多传感器、高集成化及小型化等发展趋势。目前,多功能仿人型假手的肌电控制方法远远滞后于其本体的发展,面临控制模式少、精度低、控制感官性差等缺点。本文针对多自由度假手,以多种人手运动模式的高效识别为主要目标,兹建立新型的肌电控制方法。主要研究内容包括:基于模式识别方法的肌电控制总体结构,人手预抓取模式的识别,手指姿态动作模式的识别,以及识别系统的在线学习等方面。
文中首先对国内外新型的仿人型多自由度假手、典型假手肌电控制方法进行了细致的综述。随后建立了基于模式识别方法的多功能性假手控制系统的总体结构,将其划分为人体层、生物信息采集层、模式识别层、假手本体层和反馈层5个重要组成部分,给出了各层次内相关关键技术的具体实现方法。具体地,由人体解剖学原理确定每个手指运动的功能性肌肉,以确定识别各运动模式所需的肌电电极安放位置。选用独立式模块化表面肌肤肌电电极进行肌电信号的采集。验证模式识别算法的不同搭配以获得经验最优的分类率,并建立假手虚拟操作环境进行控制算法演示及肌能训练。
确定了人手的4种基本预抓取模式,将对预抓取肌电模式的识别应用于假手抓取控制策略,使得在人手实际抓取动作未实施时即能判断假手所需采用的抓取模式。采用Teager-Kaiser Energy(TKE)算子增幅肌肉收缩时肌电信号的变化,通过判断信号统计值和引入在线后处理步骤,得到准确的预抓取肌电信号。详细验证了多种预抓取肌电信号分段方法、特征提取方法以及特征分类方法对于模式识别成功率的影响。最终采用波形长度与支持向量机作为最优的信号特征及分类器,进行预抓取模式的在线识别。假手抓取肌电控制实验表明,预抓取模式识别成功率高、延迟小,假手能正确地、快速地按规定模式抓取物体。
除了抓取模式的识别,人手手指动作的识别在假手肌电控制中也同样重要。基于对人手姿态模式的重新配置,文中采用6通道肌电信号实现了多种姿态模式的可靠识别。使用阈值决策同支持向量机决策相结合的双层决策规则,增加了参与训练的肌电瞬态特征,有效提高了人手手指在动作发生时的识别成功率。提出波动式训练方法(肌肉快节奏收缩时的样本作为各模式肌电训练数据),以提高单组次训练数据的完备性。进行了基于PC机的虚拟假手控制实验、基于DSP的嵌入式假手控制实验、以及截肢患者肌电控制实验。实验结果表明人手各运动模式识别正确,识别速度较快,控制的直观感觉较为强烈。
由于肌电信号具有时变特性,会引起识别系统识别率的下降。文中建立了正、负样本同时训练的多类数据描述分类算法,通过引入遗忘因子对惩罚因子进行加权,建立了基于超球支持向量机多类分类的剥壳增量算法。定义动作序列,并基于动作序列进行肌肉模式的在线持续性学习。对预抓取模式以及动作模式的在线学习实验表明:预抓取模式的持续性学习可以看成是其模式描述逐渐完备的过程,因此识别成功率会越来越高;而对于动作模式的学习,使用多组次的样本进行训练并不能得到识别率的提升。剥壳算法能有效的控制支持向量机复杂度的增加,且使得各组次内部分类成功率分布较高,表明融合在线学习的肌电模式识别算法具有长期稳定工作的能力。
The myoelectric hand is a type of anthropomorphic robotic hand that driven by the human electromyography (EMG) signals for amputee rehabilitation. As a multi- discipline system, it integrates the subjects of mechatronics, computer and biology together, trends to develop with more degree-of-freedoms (DOFs) and sensors, higher integration and miniaturization. However, the EMG control of the multifunctional prosthetic hand has been studied deficiently comparing with the hand bodies, which performs with a few motion modes, low accuracy and bad intuitive control feelings. Focus on the multi-DOFs prosthetic hand, this paper presents a new EMG control method aiming at efficiently recognizing more hand motion modes. The contents of this paper includes: the overall structure of the pattern recognition (PR) based EMG control; the hand prehension recognition; the hand gesture and finger motion recognition; and the online learning strategy for the recognition system, et al.
Overviews about newly developed multi-DOFs prosthetic hands and typical EMG control methods are given in detail. Then, the general structure of the PR-based EMG control system of the multifunctional prosthetic hand is established, which can be subdivided as the human layer, the biologic signal acquisition layer, the pattern recognition layer, the prosthetic hand layer and the feedback layer. Methods of implementing the critical techniques in each layer are presented. In detail, the placement of the myo-electrodes used for detecting each motion mode is determined by the muscles taking charge of the motion of each finger with the knowledge of the human biological anatomy. The modularized active EMG electrodes are adopted to acquire the surface myoelectric signals. The algorithm verification is performed to get the empirical highest recognition accuracy, and a virtual hand environment is established to demonstrate the EMG control effectiveness and give the amputee an online discipline on his muscle functionality.
Four basic preshaping modes of the hand are determined, and the recognition of the preshaping EMG modes is adopted in the hand’s grasping strategy when it attempts to hold objects. Although the grasping motion is not actually performed by the human hand, the proper strategy about the prosthetic hand how to grasp the object has been defined. The amplitude change of the EMG signal is reinforced by the Teager-Kaiser Energy (TKE) operator when the muscle contracts, then the accurate preshaping EMG data can be obtained through statistically comparing the signal’s statistical values and adding two post-processing approaches. Multifarious methods of the data segmentation, feature extraction and classification are tested and verified on their influence to the recognizability of the PR system. Ultimately, the wavelength of the signal and the support vector machine (SVM) are adopted as the empirical optimum EMG feature and classifier, respectively, to realize the online recognition of the preshaping modes. The experimental results show that the recognition algorithm can reach on both a high accuracy and a low time delay, and the prosthetic hand can grasp objects complying with the predefined prehension modes correctly and swiftly.
The recognition of the hand gestures (or finger motions) is with the same importance to the preshaping mode in the EMG control of the prosthetic hand. Based on the reconfiguration of the hand gesture modes, multiple gesture modes are recognized reliably by decoding six channels of EMG signals. The proposed double-decision strategy, which combined the threshold decision with the SVM decisions, can increase the number of the transient samples of the EMG signals, thus to improve the recognition accuracy at the time of the finger motion taking place. A new sample collecting method named“vibrating training”(collect the EMG samples while the muscle is contracting at a fast rhythm) is suggested to improve the completeness of the training samples in each EMG mode. The experiments of a PC-based online multi-mode EMG control of a virtual hand, a DSP-embedded real-time control of a five-fingered prosthetic hand, and an implementation of the EMG control method to an amputee are performed. The results show that the classification accuracy of the EMG modes is relatively high, the recognition speed is fast, and the intuitive feeling of the EMG control is intensive.
The stochastic character of the EMG will lead to a accuracy decline of the recognition system. A new multi-class data description method, which trains the positive and negative samples at the same time, is proposed. An new sample updating method named“husking”algorithm is proposed, based on the multi-class hypersphere SVM through weighting the penalty factor by a suggested forgotten factor. The“action sequence”is defined and then adopted for the on-line continual learning of the EMG modes. Experiments about the online learning of the preshaping and finger motion EMG modes show that, the persisting learning of the hand preshaping mode can be regarded as a completive process of the mode description function. For the learning process of the hand motion modes, it cannot achieve a higher recognition accuracy even using more groups of training samples. The“husking”algorithm can efficiently control the increasing number of the support vectors and make a good intragroup discriminating rate. It indicates that the EMG pattern recognition system is capable of working on a long-term stability by fusing the online learning method to it.
文中首先对国内外新型的仿人型多自由度假手、典型假手肌电控制方法进行了细致的综述。随后建立了基于模式识别方法的多功能性假手控制系统的总体结构,将其划分为人体层、生物信息采集层、模式识别层、假手本体层和反馈层5个重要组成部分,给出了各层次内相关关键技术的具体实现方法。具体地,由人体解剖学原理确定每个手指运动的功能性肌肉,以确定识别各运动模式所需的肌电电极安放位置。选用独立式模块化表面肌肤肌电电极进行肌电信号的采集。验证模式识别算法的不同搭配以获得经验最优的分类率,并建立假手虚拟操作环境进行控制算法演示及肌能训练。
确定了人手的4种基本预抓取模式,将对预抓取肌电模式的识别应用于假手抓取控制策略,使得在人手实际抓取动作未实施时即能判断假手所需采用的抓取模式。采用Teager-Kaiser Energy(TKE)算子增幅肌肉收缩时肌电信号的变化,通过判断信号统计值和引入在线后处理步骤,得到准确的预抓取肌电信号。详细验证了多种预抓取肌电信号分段方法、特征提取方法以及特征分类方法对于模式识别成功率的影响。最终采用波形长度与支持向量机作为最优的信号特征及分类器,进行预抓取模式的在线识别。假手抓取肌电控制实验表明,预抓取模式识别成功率高、延迟小,假手能正确地、快速地按规定模式抓取物体。
除了抓取模式的识别,人手手指动作的识别在假手肌电控制中也同样重要。基于对人手姿态模式的重新配置,文中采用6通道肌电信号实现了多种姿态模式的可靠识别。使用阈值决策同支持向量机决策相结合的双层决策规则,增加了参与训练的肌电瞬态特征,有效提高了人手手指在动作发生时的识别成功率。提出波动式训练方法(肌肉快节奏收缩时的样本作为各模式肌电训练数据),以提高单组次训练数据的完备性。进行了基于PC机的虚拟假手控制实验、基于DSP的嵌入式假手控制实验、以及截肢患者肌电控制实验。实验结果表明人手各运动模式识别正确,识别速度较快,控制的直观感觉较为强烈。
由于肌电信号具有时变特性,会引起识别系统识别率的下降。文中建立了正、负样本同时训练的多类数据描述分类算法,通过引入遗忘因子对惩罚因子进行加权,建立了基于超球支持向量机多类分类的剥壳增量算法。定义动作序列,并基于动作序列进行肌肉模式的在线持续性学习。对预抓取模式以及动作模式的在线学习实验表明:预抓取模式的持续性学习可以看成是其模式描述逐渐完备的过程,因此识别成功率会越来越高;而对于动作模式的学习,使用多组次的样本进行训练并不能得到识别率的提升。剥壳算法能有效的控制支持向量机复杂度的增加,且使得各组次内部分类成功率分布较高,表明融合在线学习的肌电模式识别算法具有长期稳定工作的能力。
The myoelectric hand is a type of anthropomorphic robotic hand that driven by the human electromyography (EMG) signals for amputee rehabilitation. As a multi- discipline system, it integrates the subjects of mechatronics, computer and biology together, trends to develop with more degree-of-freedoms (DOFs) and sensors, higher integration and miniaturization. However, the EMG control of the multifunctional prosthetic hand has been studied deficiently comparing with the hand bodies, which performs with a few motion modes, low accuracy and bad intuitive control feelings. Focus on the multi-DOFs prosthetic hand, this paper presents a new EMG control method aiming at efficiently recognizing more hand motion modes. The contents of this paper includes: the overall structure of the pattern recognition (PR) based EMG control; the hand prehension recognition; the hand gesture and finger motion recognition; and the online learning strategy for the recognition system, et al.
Overviews about newly developed multi-DOFs prosthetic hands and typical EMG control methods are given in detail. Then, the general structure of the PR-based EMG control system of the multifunctional prosthetic hand is established, which can be subdivided as the human layer, the biologic signal acquisition layer, the pattern recognition layer, the prosthetic hand layer and the feedback layer. Methods of implementing the critical techniques in each layer are presented. In detail, the placement of the myo-electrodes used for detecting each motion mode is determined by the muscles taking charge of the motion of each finger with the knowledge of the human biological anatomy. The modularized active EMG electrodes are adopted to acquire the surface myoelectric signals. The algorithm verification is performed to get the empirical highest recognition accuracy, and a virtual hand environment is established to demonstrate the EMG control effectiveness and give the amputee an online discipline on his muscle functionality.
Four basic preshaping modes of the hand are determined, and the recognition of the preshaping EMG modes is adopted in the hand’s grasping strategy when it attempts to hold objects. Although the grasping motion is not actually performed by the human hand, the proper strategy about the prosthetic hand how to grasp the object has been defined. The amplitude change of the EMG signal is reinforced by the Teager-Kaiser Energy (TKE) operator when the muscle contracts, then the accurate preshaping EMG data can be obtained through statistically comparing the signal’s statistical values and adding two post-processing approaches. Multifarious methods of the data segmentation, feature extraction and classification are tested and verified on their influence to the recognizability of the PR system. Ultimately, the wavelength of the signal and the support vector machine (SVM) are adopted as the empirical optimum EMG feature and classifier, respectively, to realize the online recognition of the preshaping modes. The experimental results show that the recognition algorithm can reach on both a high accuracy and a low time delay, and the prosthetic hand can grasp objects complying with the predefined prehension modes correctly and swiftly.
The recognition of the hand gestures (or finger motions) is with the same importance to the preshaping mode in the EMG control of the prosthetic hand. Based on the reconfiguration of the hand gesture modes, multiple gesture modes are recognized reliably by decoding six channels of EMG signals. The proposed double-decision strategy, which combined the threshold decision with the SVM decisions, can increase the number of the transient samples of the EMG signals, thus to improve the recognition accuracy at the time of the finger motion taking place. A new sample collecting method named“vibrating training”(collect the EMG samples while the muscle is contracting at a fast rhythm) is suggested to improve the completeness of the training samples in each EMG mode. The experiments of a PC-based online multi-mode EMG control of a virtual hand, a DSP-embedded real-time control of a five-fingered prosthetic hand, and an implementation of the EMG control method to an amputee are performed. The results show that the classification accuracy of the EMG modes is relatively high, the recognition speed is fast, and the intuitive feeling of the EMG control is intensive.
The stochastic character of the EMG will lead to a accuracy decline of the recognition system. A new multi-class data description method, which trains the positive and negative samples at the same time, is proposed. An new sample updating method named“husking”algorithm is proposed, based on the multi-class hypersphere SVM through weighting the penalty factor by a suggested forgotten factor. The“action sequence”is defined and then adopted for the on-line continual learning of the EMG modes. Experiments about the online learning of the preshaping and finger motion EMG modes show that, the persisting learning of the hand preshaping mode can be regarded as a completive process of the mode description function. For the learning process of the hand motion modes, it cannot achieve a higher recognition accuracy even using more groups of training samples. The“husking”algorithm can efficiently control the increasing number of the support vectors and make a good intragroup discriminating rate. It indicates that the EMG pattern recognition system is capable of working on a long-term stability by fusing the online learning method to it.
引文
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