手指活动影响前臂多腱肌运动单元募集模式的初步研究

发布时间：2018-02-22 02:12

本文关键词： 表面肌电信号（sEMG）募集模式手指力量运动单元动作单位（MUAP）快速独立分量分析（FastICA）　出处：《重庆大学》2012年硕士论文　论文类型：学位论文

【摘要】：人手是最灵巧的运动器官之一，神经肌肉系统对手指协同动作及力量的控制是人手灵巧活动的重要基础。神经肌肉系统对手指活动的控制是通过下行神经冲动经α运动神经元控制相应肌肉运动单元的募集和发放实现，而肌肉运动单元的募集及其动作电位的发放具体表现为可检测的肌电信号，利用肌电信号评价神经肌肉系统对手指活动的控制机理已成为重要技术手段之一。sEMG信号作为一种无创的检测方式，隐含了肌肉大量运动单元的募集信息，提取sEMG信号运动单元动作电位的发放特征，可以更有效、直接地建立神经肌肉系统与手指活动的映射关系，且已成为研究手指活动调控机理的研究热点。由于手指力量的输出依赖前臂多腱肌的控制，本课题在不同手指活动模式下，提取前臂多腱肌运动单元募集模式，分析手指活动模式对其影响，借以探索手指活动神经调控机理。本文首先针对手指力量信号的采集设计了指力信号检测装置，实现指力信号的采样、模数转换、存储等功能。采用JLBS-Ⅱ型拉压传感器将手指力量信号转换为电压信号（幅值0~20mV，频率0~30Hz），然后经由放大电路、低通滤波电路构成的信号调理电路，将其转换为0~2V的电压信号，由USB6008数据采集卡将其输入PC机，最后利用LabVIEW指力检测软件实现实时显示和存储。利用上述指力检测装置采集手指力量信号的同时，采用实验室自行设计的阵列电极采集前臂多腱肌指浅屈肌多通道sEMG信号，分析sEMG信号特征与手指力量的相关性，探讨不同手指活动模式下指浅屈肌运动单元的募集模式。首先设计了食指在6N、8N、10N、12N四个力量水平下的单指按压实验，利用6×2（行×列）电极阵列同步采集6通道指浅屈肌sEMG信号，提取各通道sEMG信号时域特征值RMS，分析手指力量、电极点位置对RMS的影响，以验证实验方法的有效性。考虑受试者个体差异和实验条件完备性，修改实验方案，即食指、中指完成20%MVC、40%MVC、60%MVC三力量水平的力量输出任务，利用7×1（行×列）阵列电极同步采集6通道指浅屈肌sEMG信号。由于sEMG信号时域特征值受外周肌肉特征的影响，选用仅与中枢神经肌肉控制系统有关的运动单元募集参数，即MUAP发放数目、MUAP发放模式、MUAP发放间隔，作为研究对象。利用FastICA算法分解sEMG信号，结合人工识别方法分离出单个MUAP波形，对MUAP发放数目、MUAP发放模式和MUAP发放间隔三个参数作统计分析，分析其与手指活动模式的相关性。通过分析RMS与手指力量、电极点位置的相关性以及MUAP发放数目、MUAP 发放模式、MUAP发放间隔与活动手指、手指力量的相关性，得到以下实验结果：（1）随手指力量水平的增加，RMS值，即肌肉激活强度，呈现出递增趋势，与前期研究结果相同，表明本文所采用的实验方法可有效用于研究手指活动的调控模式；（2）不同电极点，RMS值差异性较大，不同肌肉解剖位置肌肉激活强度不同；（3）指浅屈肌MUAP总发放数目随手指力量的增加呈现递增趋势；（4）四种类型MUAP的发放模式在食指、中指活动模式下各不相同；（5）相同力量水平下，不同类型MUAP对手指力量大贡献率不同；（6）四种类型MUAP的平均发放间隔满足理论值；（7）不同类型MUAP发放率和稳定性不同，满足低阈值运动单元发放率慢且发放稳定，高阈值运动单元发放率快却不规则；（8）食指、中指活动模式下，四种类型MUAP发放模式与手指力量的相关性同其发放间隔序列的近似熵值与手指力量的相关性一致，即运动单元发放率越快，其稳定性越差。这些初步的实验结果表明，本文采用的实验装置可有效检测肌肉不同解剖位置肌肉活动强度；结合FastICA算法和人工识别方法可有效提取sEMG信号不同MUAP波形发放信息；指浅屈肌运动单元选择性募集，受外部因素和内部因素的共同影响，其中外部因素包括手指力量水平、电极位置和手指活动模式，而内部因素包括运动单元募集阈值和所属功能分区，内外因素综合作用使其完成对手指活动的调控。
[Abstract]:Manpower is the most dexterous movement organ of power and control coordinated action of the neuromuscular system of fingers is an important basis for staff activities. Control of dexterous finger motion of the neuromuscular system by descending nerve impulse control unit via the corresponding muscle motor neurons the recruitment and firing, and payment of specific performance and raise the action potential muscle movement unit for EMG signal can be detected, using EMG signals on neuromuscular control mechanism of finger activities has become an important technical means of.SEMG signal as a non-invasive detection method, implicit muscle mass motor unit recruitment information release, extracting sEMG signal features of motor unit action potentials, can more effective, the establishment of direct mapping between the neuromuscular system and the finger movement, and has become a research of finger activity regulating machine The research focus of the theory. Because the output of finger strength depends on the control of forearm multiple tendons, this topic extracts the motion unit recruitment mode of the tendons of the forearm under different finger movement modes, and analyzes the influence of finger movement mode, so as to explore the regulation mechanism of the motor nerves of fingers.
In this paper, finger force signal acquisition is designed to realize signal detection device, refers to the force signal sampling, analog-to-digital conversion, storage and other functions. Using JLBS- type pull pressure sensor converts the finger force signal is a voltage signal (amplitude 0~20mV, frequency 0~30Hz), then through the signal amplifying circuit, low-pass filter circuit a conditioning circuit converts the voltage of the 0~2V signal by USB6008 data acquisition card as the input of PC machine, finally using LabVIEW software to realize the real-time display of finger force detection and storage.
The finger force detection device to acquisition of finger force signals at the same time, the laboratory design of electrode array acquisition of multitendoned forearm superficial flexor muscle of multi-channel sEMG signals, correlation analysis of the characteristic of sEMG signal and finger strength, to explore different finger activities under the superficial flexor motor unit recruitment mode. The first design index in 6N 8N, 10N, single finger pressing experiment 12N four power levels, using 6 x 2 (row * column) electrode array 6 channel synchronous acquisition of superficial flexor sEMG signal from the channel sEMG signal value RMS, analysis of finger strength, the influence of electrode position on the RMS, in order to validate experimental method. Considering the individual differences between participants and experimental conditions of completeness, modify the experiment scheme, instant, to complete 20%MVC, 40%MVC, 60%MVC three power output power level, using 7 x 1 (row * column) with electrode array Step 6 channel acquisition superficial flexor sEMG signal. Because of the influence of peripheral muscle characteristics in the time domain characteristics of sEMG signal, the only central nervous system related muscle motion control unit to raise parameters, namely MUAP numbers, MUAP distribution model, MUAP distribution interval, as the research object. By using the FastICA decomposition algorithm combined with sEMG signal. The artificial recognition method of isolated single MUAP waveform, the MUAP numbers of MUAP, MUAP and the mode of payment issued between three parameters for statistical analysis, analysis of its correlation with finger activity pattern. Through the analysis of the RMS and finger force, electrode position and the correlation between the MUAP numbers, MUAP
The mode of payment, MUAP payment intervals and the activity between fingers, finger strength, get the following results: (1) increase with the finger strength level of RMS value, muscle activation intensity, showing an increasing trend, the same and the previous research results, experimental results show that this method can be effectively used to control model of finger movement; (2) different electrode, RMS value differences of different muscle strength in different anatomical position of muscle activation; (3) to increase the number of MUAP issued with the finger flexor muscle strength increased; (4) four types of MUAP distribution pattern in the index finger, middle finger activity pattern under different; (5) the same power level, different types of MUAP on large finger strength contribution rate; (6) the average firing interval of four types of MUAP meet the theoretical value; (7) different types of MUAP firing rate and stability, meet the low threshold. Pneumatic unit firing rate is slow and stable release, high threshold motor unit firing rate quickly but not rule; (8) the index finger, middle finger activity pattern, four types of MUAP firing pattern and finger strength correlation correlation with the distribution interval sequence of approximate entropy and finger strength, the motor unit firing rate more quickly and the worse stability. These preliminary experimental results show that the experimental device used in this paper can effectively detect the muscle strength of different anatomical position of muscle activity; the combination of FastICA algorithm and artificial recognition method can effectively extract the sEMG signals of different MUAP waveform distribution information; superficial flexor motor unit recruitment is influenced by selective, external and internal factors among them, the external factors include finger strength, electrode position and finger activity pattern, while the internal factors including motion unit recruitment threshold and belongs to functional area, inside and outside The combination of factors makes it complete the control of the movement of the finger.

【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R318.0

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