本文關(guān)鍵詞： 表面肌電信號(hào)（sEMG） 募集模式 手指力量 運(yùn)動(dòng)單元?jiǎng)幼鲉挝唬∕UAP） 快速獨(dú)立分量分析（FastICA）　出處：《重慶大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：人手是最靈巧的運(yùn)動(dòng)器官之一，神經(jīng)肌肉系統(tǒng)對手指協(xié)同動(dòng)作及力量的控制是人手靈巧活動(dòng)的重要基礎(chǔ)。神經(jīng)肌肉系統(tǒng)對手指活動(dòng)的控制是通過下行神經(jīng)沖動(dòng)經(jīng)α運(yùn)動(dòng)神經(jīng)元控制相應(yīng)肌肉運(yùn)動(dòng)單元的募集和發(fā)放實(shí)現(xiàn)，而肌肉運(yùn)動(dòng)單元的募集及其動(dòng)作電位的發(fā)放具體表現(xiàn)為可檢測的肌電信號(hào)，利用肌電信號(hào)評價(jià)神經(jīng)肌肉系統(tǒng)對手指活動(dòng)的控制機(jī)理已成為重要技術(shù)手段之一。sEMG信號(hào)作為一種無創(chuàng)的檢測方式，隱含了肌肉大量運(yùn)動(dòng)單元的募集信息，提取sEMG信號(hào)運(yùn)動(dòng)單元?jiǎng)幼麟娢坏陌l(fā)放特征，可以更有效、直接地建立神經(jīng)肌肉系統(tǒng)與手指活動(dòng)的映射關(guān)系，且已成為研究手指活動(dòng)調(diào)控機(jī)理的研究熱點(diǎn)。由于手指力量的輸出依賴前臂多腱肌的控制，本課題在不同手指活動(dòng)模式下，提取前臂多腱肌運(yùn)動(dòng)單元募集模式，分析手指活動(dòng)模式對其影響，借以探索手指活動(dòng)神經(jīng)調(diào)控機(jī)理。 本文首先針對手指力量信號(hào)的采集設(shè)計(jì)了指力信號(hào)檢測裝置，實(shí)現(xiàn)指力信號(hào)的采樣、模數(shù)轉(zhuǎn)換、存儲(chǔ)等功能。采用JLBS-Ⅱ型拉壓傳感器將手指力量信號(hào)轉(zhuǎn)換為電壓信號(hào)（幅值0~20mV，頻率0~30Hz），然后經(jīng)由放大電路、低通濾波電路構(gòu)成的信號(hào)調(diào)理電路，將其轉(zhuǎn)換為0~2V的電壓信號(hào)，由USB6008數(shù)據(jù)采集卡將其輸入PC機(jī)，最后利用LabVIEW指力檢測軟件實(shí)現(xiàn)實(shí)時(shí)顯示和存儲(chǔ)。 利用上述指力檢測裝置采集手指力量信號(hào)的同時(shí)，采用實(shí)驗(yàn)室自行設(shè)計(jì)的陣列電極采集前臂多腱肌指淺屈肌多通道sEMG信號(hào)，分析sEMG信號(hào)特征與手指力量的相關(guān)性，探討不同手指活動(dòng)模式下指淺屈肌運(yùn)動(dòng)單元的募集模式。首先設(shè)計(jì)了食指在6N、8N、10N、12N四個(gè)力量水平下的單指按壓實(shí)驗(yàn)，利用6×2（行×列）電極陣列同步采集6通道指淺屈肌sEMG信號(hào)，提取各通道sEMG信號(hào)時(shí)域特征值RMS，分析手指力量、電極點(diǎn)位置對RMS的影響，以驗(yàn)證實(shí)驗(yàn)方法的有效性�？紤]受試者個(gè)體差異和實(shí)驗(yàn)條件完備性，修改實(shí)驗(yàn)方案，即食指、中指完成20%MVC、40%MVC、60%MVC三力量水平的力量輸出任務(wù)，利用7×1（行×列）陣列電極同步采集6通道指淺屈肌sEMG信號(hào)。由于sEMG信號(hào)時(shí)域特征值受外周肌肉特征的影響，選用僅與中樞神經(jīng)肌肉控制系統(tǒng)有關(guān)的運(yùn)動(dòng)單元募集參數(shù)，即MUAP發(fā)放數(shù)目、MUAP發(fā)放模式、MUAP發(fā)放間隔，作為研究對象。利用FastICA算法分解sEMG信號(hào)，結(jié)合人工識(shí)別方法分離出單個(gè)MUAP波形，對MUAP發(fā)放數(shù)目、MUAP發(fā)放模式和MUAP發(fā)放間隔三個(gè)參數(shù)作統(tǒng)計(jì)分析，分析其與手指活動(dòng)模式的相關(guān)性。通過分析RMS與手指力量、電極點(diǎn)位置的相關(guān)性以及MUAP發(fā)放數(shù)目、MUAP 發(fā)放模式、MUAP發(fā)放間隔與活動(dòng)手指、手指力量的相關(guān)性，得到以下實(shí)驗(yàn)結(jié)果：（1）隨手指力量水平的增加，RMS值，即肌肉激活強(qiáng)度，呈現(xiàn)出遞增趨勢，與前期研究結(jié)果相同，表明本文所采用的實(shí)驗(yàn)方法可有效用于研究手指活動(dòng)的調(diào)控模式；（2）不同電極點(diǎn)，RMS值差異性較大，不同肌肉解剖位置肌肉激活強(qiáng)度不同；（3）指淺屈肌MUAP總發(fā)放數(shù)目隨手指力量的增加呈現(xiàn)遞增趨勢；（4）四種類型MUAP的發(fā)放模式在食指、中指活動(dòng)模式下各不相同；（5）相同力量水平下，不同類型MUAP對手指力量大貢獻(xiàn)率不同；（6）四種類型MUAP的平均發(fā)放間隔滿足理論值；（7）不同類型MUAP發(fā)放率和穩(wěn)定性不同，滿足低閾值運(yùn)動(dòng)單元發(fā)放率慢且發(fā)放穩(wěn)定，高閾值運(yùn)動(dòng)單元發(fā)放率快卻不規(guī)則；（8）食指、中指活動(dòng)模式下，四種類型MUAP發(fā)放模式與手指力量的相關(guān)性同其發(fā)放間隔序列的近似熵值與手指力量的相關(guān)性一致，即運(yùn)動(dòng)單元發(fā)放率越快，其穩(wěn)定性越差。這些初步的實(shí)驗(yàn)結(jié)果表明，本文采用的實(shí)驗(yàn)裝置可有效檢測肌肉不同解剖位置肌肉活動(dòng)強(qiáng)度；結(jié)合FastICA算法和人工識(shí)別方法可有效提取sEMG信號(hào)不同MUAP波形發(fā)放信息；指淺屈肌運(yùn)動(dòng)單元選擇性募集，受外部因素和內(nèi)部因素的共同影響，其中外部因素包括手指力量水平、電極位置和手指活動(dòng)模式，而內(nèi)部因素包括運(yùn)動(dòng)單元募集閾值和所屬功能分區(qū)，內(nèi)外因素綜合作用使其完成對手指活動(dòng)的調(diào)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.

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0

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本文編號(hào)：1523398