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Liu, Lukai. "A Study of Myoelectric Signal Processing." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/34.
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This dissertation of various aspects of electromyogram (EMG: muscle electrical activity) signal processing is comprised of two projects in which I was the lead investigator and two team projects in which I participated. The first investigator-led project was a study of reconstructing continuous EMG discharge rates from neural impulses. Related methods for calculating neural firing rates in other contexts were adapted and applied to the intramuscular motor unit action potential train firing rate. Statistical results based on simulation and clinical data suggest that performances of spline-based methods are superior to conventional filter-based methods in the absence of decomposition error, but they unacceptably degrade in the presence of even the smallest decomposition errors present in real EMG data, which is typically around 3-5%. Optimal parameters for each method are found, and with normal decomposition error rates, ranks of these methods with their optimal parameters are given. Overall, Hanning filtering and Berger methods exhibit consistent and significant advantages over other methods. In the second investigator-led project, the technique of signal whitening was applied prior to motion classification of upper limb surface EMG signals previously collected from the forearm muscles of intact and amputee subjects. The motions classified consisted of 11 hand and wrist actions pertaining to prosthesis control. Theoretical models and experimental data showed that whitening increased EMG signal bandwidth by 65-75% and the coefficients of variation of temporal features computed from the EMG were reduced. As a result, a consistent classification accuracy improvement of 3-5% was observed for all subjects at small analysis durations (< 100 ms). In the first team-based project, advanced modeling methods of the constant posture EMG-torque relationship about the elbow were studied: whitened and multi-channel EMG signals, training set duration, regularized model parameter estimation and nonlinear models. Combined, these methods reduced error to less than a quarter of standard techniques. In the second team-based project, a study related biceps-triceps surface EMG to elbow torque at seven joint angles during constant-posture contractions. Models accounting for co-contraction estimated that individual flexion muscle torques were much higher than models that did not account for co-contraction.
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Englehart, K. "Signal representation for classification of the transient myoelectric signal." Thesis, University of New Brunswick, 1998. http://hdl.handle.net/1882/808.
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Englehart, Kevin. "Signal representation for classification of the transient myoelectric signal." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0016/NQ46463.pdf.
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Bach, Per Ferdinand. "Myoelectric signal features for upper limb prostheses." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8985.
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<p>In the last couple of years The Institute of Cybernetics at NTNU, Norway, has based its research on the SVEN work carried out in Sweden in the late 1970’s. The SVEN hand was an on/off-controlled upper limb prosthesis based on electromyographic (EMG) signals. This master thesis is a part of the renewed and continuing research. This study will try to identify signal features that are beneficial in a proportional control of a multi-function upper limb prosthesis. The intent is to identify a set of signal features that could be implemented in a practical proportional control system to enhance the movement functions of the prosthesis such that it more closely mimic the movements of a normal upper limb. The data set used in this paper consist of EMG signals and VICON angle data recorded by Fougner (2007). A short explanation will be given on how to acquire such data. A brief introduction on feature selection defines the properties of a wrapper and filter approach in search for a feature subset. Wavelets properties are explained and two wavelet techniques are used in order to obtain more information from the EMG signal in addition to existing features. From this, we search for a subset of features that will let us use a mapping function that estimates a correct motion with respect to the features fed to it. The Cosine Similarity Transform (CST) and the Correlation coefficient (CORR) will in addition to RMSE be investigated in order to find an optimal performance indicator. With a good and reliable indicator we may find a suitable subset. EWC-WAVE were found to be the best subset according to both CST and RMSE. Based upon the information obtained from each performance indicator, it is suggested that CST should be carried out as a measure of accuracy on how to map data in the future. There are still unsolved problems. Some of the angles we tried to estimate with a neural network suffered and produced non-informative data. This indicate that one should add more hidden nodes to a neural network when more features are used as input. We have obtained indications that we do need to combine feature subsets in order to obtain higher accuracy of the estimated signal. It is proposed that a post-processing technique should be developed and used subsequent to the pattern recognition methods in order to achieve a signal that better reflects the estimation and may be used as a control signal for a prosthesis. Hopefully will these findings help improve future work to achieve an enhanced proportional control for a real prosthesis.</p>
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Del, Boca Adrian. "Myoelectric signal recognition using artificial neural networks in real time." FIU Digital Commons, 1993. http://digitalcommons.fiu.edu/etd/2764.
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Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the EMG signal by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface regardless of electrodes location, strength of remaining muscle activity or even personal conditions. Adaptability is a natural and important characteristic of artificial neural networks. This research work is restricted to the development of a real-time application of artificial neural network to the EMG signature recognition. Through this new approach, EMG features extracted by Fourier analysis are presented to a multilayer perceptron type neural network. The neural network learns the most relevant features of the control signal. For real-time operation, a digital signal processor operates over the resulting set of weights from the learning process, and maps the incoming signal to the stimulus control domain. Results showed a highly accurate discrimination of the EMG signal over interference patterns.
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McCool, Paul. "Surface myoelectric signal analysis and enhancement for improved prosthesis control." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=23209.
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In this thesis, novel signal processing and machine learning techniques are presented in the field of myoelectric control. Specifically, algorithms for activity detection, noise identification and noise reduction are introduced, evaluated and discussed. The ultimate aim has been to develop algorithms to improve the performance of prosthetic control systems that use myoelectric signals. Such systems must be an ability to distinguish between electromyographic signals and background noise. For this, the behaviour of One-Dimensional Local Binary Pattern histograms were used to identify the presence of myoelectric activity in recorded signals that originated from electrode sensors on the surface of the skin. This technique was compared against two other activity detection methods and it was found to give better performance in some circumstances. In particular, a lower False Positive Rate was achieved. Noise is always present in myoelectric signals, and if it can be identified then step s can be taken to quantify and/or mitigate it. Pattern recognition was used to identify a single noise type in pre-recorded myoelectric signals. A set of Radial Basis Function Support Vector Machines were trained and tested on clean myoelectric signals that have been artificially contaminated with five typical noise types. The behaviour of the features and the nature of the confusion are discussed. Identification was shown to be possible, but confusion between noise types grew as the SNR increased. Spectral Enhancement, which is normally used on speech signals, is applied to myoelectric signals in an attempt to mitigate noise. Spectral Enhancement based on Improved Minima Controlled Recursive Averaging (IMCRA) was found to improve the classification accuracy, and by corollary the signal quality, with signals that had white noise artificially added (which can be present in recorded myoelectric signals) and with intrinsically noisy signals. The improvement was higher when fewer channels were used.
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Thomson, Kyle E. "Hardware considerations of space-time processing in implantable neuroprosthetic devices." Diss., Connect to online resource - MSU authorized users, 2006.
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Thesis (M.S.)--Michigan State University. Dept. of Electrical and Computer Engineering, 2006.<br>Title from PDF t.p. (viewed on Nov. 20, 2008) Includes bibliographical references (p. 51-52). Also issued in print.
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Hofmann, David [Verfasser], Florentin [Akademischer Betreuer] Wörgötter, and Dario [Akademischer Betreuer] Farina. "Myoelectric Signal Processing for Prosthesis Control / David Hofmann. Gutachter: Florentin Wörgötter ; Dario Farina. Betreuer: Florentin Wörgötter." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2015. http://d-nb.info/1066427496/34.
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Luo, R. "Can the voluntary drive to a paretic muscle be estimated from the myoelectric signal during stimulation?" Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1409755/.
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Patients with SCI sometimes recover lost function after using FES. This phenomenon, known as the carry-over effect, is not fully understood. One theory used to explain this mechanism is that electrical stimulation of the peripheral nerve causes antidromic action potentials to reach the anterior horn cells in time with the patient’s voluntary effort. This may reinforce the motor pathways and consequently restore voluntary control. However, the theory has never been properly tested and testing requires a method of measuring the voluntary drive. This project aims to find out whether it is possible to estimate the voluntary drive from measured myoelectric signals. The project is based on an FES cycling system with the ability to adjust the stimulation intensity relating to the corresponding voluntary drive. In paretic muscles, the weak voluntary contraction produces an EMG response. The EMG signal cannot be used directly as an indication of the voluntary drive because of the presence of stimulus artefact and reflexes. Two methods were investigated to estimate the voluntary drive. A time domain method was tested using RMS EMG extracted from a range of time windows following the stimulation pulse. This approach was unsatisfactory because the large variations seen in the RMS EMG amplitudes for the same power output as well as the low sensitivity of it to the change of power output. A frequency domain approach was then tested using coherence between co-contracting muscles. It was encouraging to see that the area under the coherence curve in the β band reflected changes in the power output level. However, further tests showed that this area was also greatly influenced by exercise time, becoming unpredictable after 3 minutes. In conclusion, neither of the two methods of using the myoelectric signal from muscles under stimulation is practical for the estimation of voluntary drive.
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Michel, Aubé. "Influence of pedalling rate and resistance on the deterministic component of the myoelectric signal during ergometer cycling." Thesis, University of New Brunswick, 1997. http://hdl.handle.net/1882/496.
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Aubé, Michel. "Influence of pedalling rate and resistance on the deterministic component of the myoelectric signal during ergometer cycling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23777.pdf.
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Cechetto, Angela D. "The effects of four physiological factors on the non-stationarities in the mean frequency of a myoelectric signal." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0032/MQ65478.pdf.
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Butler, Nickolas Andrew. "Development of a Myoelectric Detection Circuit Platform for Computer Interface Applications." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/1981.
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Personal computers and portable electronics continue to rapidly advance and integrate into our lives as tools that facilitate efficient communication and interaction with the outside world. Now with a multitude of different devices available, personal computers are accessible to a wider audience than ever before. To continue to expand and reach new users, novel user interface technologies have been developed, such as touch input and gyroscopic motion, in which enhanced control fidelity can be achieved. For users with limited-to-no use of their hands, or for those who seek additional means to intuitively use and command a computer, novel sensory systems can be employed that interpret the natural electric signals produced by the human body as command inputs. One of these novel sensor systems is the myoelectric detection circuit, which can measure electromyographic (EMG) signals produced by contracting muscles through specialized electrodes, and convert the signals into a usable form through an analog circuit. With the goal of making a general-purpose myoelectric detection circuit platform for computer interface applications, several electrical circuit designs were iterated using OrCAD software, manufactured using PCB fabrication techniques, and tested with electrical measurement equipment and in a computer simulation. The analog circuit design culminated in a 1.35” x 0.8” manufactured analog myoelectric detection circuit unit that successfully converts a measured EMG input signal from surface skin electrodes to a clean and usable 0-5 V DC output that seamlessly interfaces with an Arduino Leonardo microcontroller for further signal processing and logic operations. Multiple input channels were combined with a microcontroller to create an EMG interface device that was used to interface with a PC, where simulated mouse cursor movement was controlled through the voluntary EMG signals provided by a user. Functional testing of the interface device was performed, which showed a long battery life of 44.6 hours, and effectiveness in using a PC to type with an on-screen keyboard.
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Favieiro, Gabriela Winkler. "Desenvolvimento de um sistema neuro-fuzzi para análise de sinais mioelétricos do segmento mão-braço." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/71568.
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Pesquisas científicas no campo da engenharia de reabilitação estão proporcionando cada vez mais mecanismos que visam ajudar pessoas portadoras de alguma deficiência física a executar tarefas simples do dia-a-dia. Com isso em mente, esse trabalho tem a finalidade de desenvolver um sistema que utiliza sinais musculares e redes neuro-fuzzy para a caracterização de determinados movimentos de um braço humano, com o objetivo de possibilitar futuramente a integração em sistemas de reabilitação. Ensaios preliminares demonstraram que para a caracterização de movimentos simples realizados por um braço humano, o uso exclusivo de técnicas simples de processamento de sinal é suficiente, como a utilização do valor rms. No entanto, para a caracterização de movimentos complexos é necessário um processamento mais robusto do sinal. Para isso foi desenvolvido um sistema experimental que adquire, através de um eletromiógrafo (EMG) de 8 canais, o sinal mioelétrico com eletrodos de superfície posicionados em lugares estratégicos do braço. O sinal é adquirido utilizando como estímulo um modelo virtual que demonstra ao usuário os movimentos do segmento mão-braço que devem ser executados de forma aleatória. Finalmente, com o uso de uma rede neuro-fuzzy, que possibilita a distinção tanto de movimentos simples como de movimentos compostos, se adaptando a diferentes usuários, os movimentos executados foram caracterizados em 12 movimentos distintos, previamente definidos, com uma taxa de acerto médio de 65%.<br>The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms to help people with a disability to perform simple tasks of day-to-day. With that in mind, this work aims to develop an experimental robotic prosthesis in order to implement, in the same, a control system that uses muscle signals and neuro-fuzzy networks for characterization of certain movements of a human arm, in order to enable further integration in rehabilitation systems. Preliminary tests showed that for the characterization of simple movements performed by a human arm, the exclusive use of simple techniques of signal processing is sufficient, as the use of the rms value. However, for the characterization of complex movements is required a more robust signal processing. For this was developed an experimental system that acquires through an electromyography (EMG) of 8 channels, the myoelectric signal with surface electrodes positioned in strategic places of the arm. The acquired signal uses, as a stimulus, a virtual model that demonstrates the hand-arm segment movements to be executed by the user at random. Finally, through a neuro-fuzzy network, which enables the distinction of both simple and compound movements, self-adapting to different users, the movements performed were characterized in 12 distinct movements, previously defined, with an average accuracy of 65%.
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Moreira, Marcelo Hubner. "Caracterização da fadiga a partir do processamento de sinais mioelétricos e sua utilização no diagnóstico da síndrome da fibromialgia." Universidade Federal do Espírito Santo, 2013. http://repositorio.ufes.br/handle/10/5756.
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Previous issue date: 2013-01-20<br>This work aims to characterize the fatigue from myoelectric signals and use them as an aid to the diagnosis of rheumatic diseases such as Fibromyalgia. The condition for this is the analysis of muscle fatigue. Through the evaluation of myoelectric signals, the behavior of muscle in some work situations was measured, such as isotonic and isometric muscle contraction, which describes the static and dynamic motor behavior. With the myoelectric signals, digital filtering techniques were applied to mitigate the noise corrupting the myoelectric signal. Then some algorithms were implemented to detect fatigue. With that, a protocol for assessing motor response based on the condition of muscle fatigue was established. In this situation, with the working muscle, the myoelectric signal acquisition was made from surface electrodes, using a commercial acquisition system. The data were processed in MATLAB R platform; algorithms were implemented for the identification of fatigue, such as RMS, MNF, ARV, MDF and AIF. In the final result, it was found that for both isometric tasks and isotonic tasks, it is recommended the use of constant weight with 60% of MCV, using MNF and RMS indicators, which were the most consistent indicators among them<br>Este trabalho tem a finalidade de caracterizar a fadiga a partir da coleta de sinais mioelétricos e usá-lo como ajuda no diagnóstico de doenças reumáticas, como a fibromialgia. A condição para tal é a análise da fadiga muscular. Através da avaliação dos sinais mioelétricos, foi verificado o comportamento do músculo em algumas situações de trabalho, como a contração muscular isotônica e isométrica, que descreve o comportamento motor dinâmico e estático. Com os sinais obtidos, foram aplicadas técnicas de filtragem digital a fim de atenuar os ruídos que corrompem o sinal mioelétrico. Em seguida, foi estabelecida a implementação dealgoritmos para detectar a fadiga. Com isso, pôde-se estabelecer um protocolo de avaliação motora baseada na resposta do músculo à condição de fadiga. Nessa situação de trabalho muscular, a aquisição de sinais mioelétricos foi feita a partir de eletrodos de superfície e aparelho comercial (EMG System do Brasil). Os dados foram processados em plataforma MATLAB R, onde foram implementados algoritmos para a identificação de fadiga, tais como RMS, MNF, ARV, MDF e AIF. No resultado final, foi constatado que, tanto para tarefas isométricas quanto para tarefas isotônicas, é recomendado a utilização de peso constante com 60% da MCV, segundo indicadores MNF e RMS, que foram os mais coerentes dos indicadores pesquisados.
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Cene, Vinicius Horn. "Desenvolvimento de um projeto de experimentos para a caracterização de sinais mioelétricos através do uso de regressão logística." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/140519.
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Através dos dispositivos e técnicas desenvolvidas na área da Instrumentação Biomédica é possível oferecer um tratamento ou de forma geral soluções que permitam uma vivência mais plena em sociedade para pessoas que possuem algum tipo de deficiência ou doença. Com o aumento da capacidade computacional nos últimos anos foi possível desenvolver muitas técnicas e dispositivos apoiados em processamento digital de sinais e há um grande interesse pelo desenvolvimento de interfaces mais naturais, como sinais biológicos para o controle de próteses e dispositivos. Este trabalho tem por objetivo apresentar o desenvolvimento de um método de Inteligência Computacional baseado em Regressão Logística capaz de classificar 17 movimentos do segmento mão-braço realizados pelos voluntários do estudo através do processamento do sinal mioelétrico (SME) adquiridos dos sujeitos em questão. Adicionalmente é realizada uma avaliação da influência de alguns dos canais, características do sinal e movimentos executados na taxa de acerto global. Para a realização do sistema foi utilizado um aparato experimental capaz de adquirir os SME através de 12 canais utilizando eletrodos não invasivos e posteriormente digitalizá-los. Logo após efetua-se a extração das três características utilizadas no trabalho, que servem de entrada para o método de Regressão Logística. Para este estudo foram processados três bancos de dados que perfazem um total de 50 voluntários. A taxa média de acerto alcançada foi de 70,1%, considerando todas as variações de testes realizados enquanto a média para os melhores casos de cada variação de entrada realizada foi de 92,5%.<br>Through the devices and techniques developed in the field of Biomedical Instrumentation commonly is possible to offer treatment or solutions to provide a more pleasurable experience in society for people who have a disability or illness. With increasing computing capability in recent years, it has been possible to develop many techniques and devices supported by digital signal processing, and there is a great interest in the development of more natural interfaces, such as biological signals for the control of devices and prostheses. This work aims to present the development of a computational intelligence method based on Logistic Regression able to classify 17 movements of the hand-arm segment performed by the subjects of this study through the processing of the myoelectric signal (SME) acquired from the subject in question. Additionally, an assessment of the influence of some of the combination of the channels, signal characteristics and movements performed in the overall hit rate is additionally performed. To conduct the system has built an experimental apparatus able to acquire the SME through 12 channels using non-invasive electrodes and scan them. Thereafter there is a three features extraction from the signal which serves as input to the Logistic Regression method. For this study were processed three databases that compose 50 volunteers. The average hit rate achieved was 70.1%, considering all tests variations while the average for the best cases for each input variation performed was 92,5 %.
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Talebinejad, Mehran. "Fractal analysis of myoelectric signals." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27184.
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In this thesis, we study fractal behavior of myoelectric signals (MESs). Mathematics and definitions behind fractal behavior is presented. Different methods of estimating the fractal dimension are discussed, including time domain-based methods (i.e. Katz method, Box-Counting method) and spectrum based methods (i.e. Power Spectrum Slope Method (PSSM) and General Power Spectrum Method (GPSM)). The GPSM is introduced in context of MESs for the first time. Using simulated MESs effects of its parameters (i.e. number of active motor units (MUs), firing rate and depth of active MUs) on estimated fractal dimension (eFD) and estimated fractal indicators (eFI) are analyzed. Spectrum based methods demonstrate characteristics that suggest superiority in discerning force effects (i.e. number of active MUs and firing rate) and geometric effects (i.e. depth of active MUs). Fractal behavior of MESs during Isometric Constant Force Contractions (ICFC) at different force and joint angles are analyzed. Results of the spectrum based methods suggest that they could possibly be used to estimate the joint angle independent of force. Fractal behavior of MESs during Isometric Voluntary Contractions (IVC) at different force and joint angles are analyzed as well. Results, although highly variable, remain consistent with the simulated results.
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Bagesteiro, Leia Bernardi. "Estudo da intensidade elétrica de músculos do membro superior durante movimentos do segmento mão-braço de indivíduos amputados." reponame:Repositório Institucional da UFABC, 2014.
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Orientadora: Profa. Dra. Léia Bernardi Bagesteiro<br>Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2014.<br>A maioria das amputações traumáticas do membro superior ocorre em níveis do punho e da mão. O uso de sinais bioelétricos, como os capturados pela eletromiografia de músculos residuais no segmento mão-braço amputado, já é uma realidade, porém as taxas de rejeição das próteses mioelétricas ainda são elevadas. Um dos maiores desafios atuais é melhorar o desempenho das próteses robóticas a fim de que a interface homem-máquina seja o mais natural possível. O presente estudo teve por objetivo avaliar a intensidade elétrica dos músculos residuais do coto durante movimentos do segmento mão-braço de indivíduos amputados de membro superior. Foram caracterizados diferentes movimentos-alvo a fim de auxiliar no conhecimento da ação muscular após amputação. Oito canais de eletromiografia foram posicionados no membro superior e coto: quatro no braço e quatro no antebraço. Nove movimentos-alvo contínuos foram realizados duas vezes em cada uma das duas séries de movimentos avaliadas. Foram analisados o coto (grupo experimental membro amputado), o membro contralateral à amputação (grupo experimental membro não amputado) e um grupo controle. Todos os grupos apresentaram as mesmas etapas de coleta, desde o posicionamento dos eletrodos até a análise do sinal eletromiográfico. Os dados foram comparados para maior compreensão dos movimentos do membro superior nesses grupos.
Os resultados apresentaram diferença entre os grupos. A musculatura de braço teve maior intensidade de ativação elétrica no grupo controle. Por sua vez, o grupo experimental membro amputado teve maior intensidade de ativação em musculaturas extensoras e maior diferenciação dos canais do antebraço. Contudo, o grupo experimental membro não amputado foi o que mais se diferenciou nas diferentes análises, sendo observado menor intensidade de ativação elétrica no braço e antebraço. Conclui-se que a intensidade de ativação dos músculos residuais do coto difere do membro não amputado.<br>Most traumatic upper-limb amputations occur at the wrist and hand levels. Bioelectric signals, such as the ones captured by electromyography at the amputated hand-arm segment are already a reality, yet rejection rates of myoelectric prostheses are still high. The main challenge is to improve the performance of robotic prostheses enabling a more natural man-machine interface.
This study evaluated the electrical magnitude of residual muscles during hand-arm movements of upper limb amputee. Different movements were chosen to promote muscle activation knowledge after amputation. Eight electromyography channels were positioned on the upper-limb and stump: four at the upper arm and four at the forearm. Nine continuous movements were performed twice on each of the two series evaluated. Three groups were analyzed: amputee group (experimental group: amputated limb), contralateral limb amputation (experimental group: non-amputated limb) and control group. All groups had the same data collection phases, electrodes positioning and eletromyographic signals analysis. Data were compared for better understanding of upper limb movements.
The results showed difference between groups. The upper arm muscles had greater magnitude of electrical activation in the control group. The experimental group: amputated limb had greater magnitude of electrical activation in the extensor musculature and greater difference from the forearm channels. Moreover the experimental group: non-amputated limb was the most distinguished in comparison with the other two groups on different analyzes, with lower magnitude of electrical activation observed in the upper arm and forearm. It is concluded that the magnitude of activation of the residual muscles in the amputated limb differs from non-amputated limb.
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Ortolan, Rodrigo Lício. "Estudo e avaliação de técnicas de processamento do sinal mioelétrico para o controle de sistemas de reabilitação." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-19112002-153337/.
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Este trabalho tem a finalidade de analisar algumas técnicas de processamento do sinal mioelétrico, de forma a possibilitar uma posterior implementação de um circuito, que reconheça este sinal e apresente como saída um sinal de controle a ser utilizado em sistemas de reabilitação. Foram simuladas e avaliadas três técnicas de filtragem para o sinal mioelétrico, a fim de atenuar a interferência dos principais ruídos que corrompem este sinal. As técnicas avaliadas foram: filtragem digital clássica; cancelamento de ruído adaptativo e reconstrução do sinal por meio das componentes obtidas pela transformada wavelet. Também foi implementado e analisado um sistema simplificado de reconhecimento dos padrões para este sinal, realizado por meio de uma rede neural artificial, em que foi aplicado em sua entrada o próprio sinal mioelétrico e não suas características obtidas por processamentos matemáticos. Diante dos resultados obtidos os canceladores de ruído adaptativos apresentaram melhores resultados com relação às outras técnicas de filtragem. Apesar de não ter sido adequada para a filtragem, a transformada wavelet mostrou-se uma poderosa ferramenta de análise de sinais, em virtude da sua característica multiresolução. A técnica utilizada para reconhecer os padrões do sinal mostrou bons resultados com os sinais analisados.<br>This work has the purpose to analyze some techniques for myoelectric signal processing, towards a subsequent implementation of a circuit which can recognize this signal and present as output a control signal to be used in rehabilitation systems. Simulation and evaluation of three filtering techniques for the myoelectric signal were done in order to attenuate the main interferences of noises which corrupt this signal. The evaluated techniques were: classic digital filtering; adaptive noise cancelling and the signal reconstruction through the obtained components by the wavelet transform. A simplified system of pattern recognition for this signal also was implemented and analyzed, accomplished through an artificial neural network. The myoelectric signal itself was applied to the input instead of its characteristics obtained by mathematical processing. According to the results obtained the adaptive noise cancelling presented better results in comparison to the other filtering techniques. Despite not being adequate for filtering, the wavelet transform proved to be a powerful tool for signal analysis, by virtue of its multiresolution characteristics. The technique used to recognize the signal patterns has shown good results with the analyzed signals.
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Atsma, Willem Jentje. "Classification of myoelectric signals using neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29968.pdf.
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Ju, Peter M. (Peter Ming-Wei) 1977. "Classification of finger gestures from myoelectric signals." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9074.
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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 73-75).<br>Electromyographic signals may provide an important new class of user interface for consumer electronics. In order to make such interfaces effective, it will be crucial to map EMG signals to user gestures in real time. The mapping from signals to gestures will vary from user to user, so it must be acquired adaptively. In this thesis, I describe and compare three methods for static classification of EMG signals. I then go on to explore methods for adapting the classifiers over time and for sequential analysis of the gesture stream by combining the static classification algorithm with a hidden Markov model. I conclude with an evaluation of the combined model on an unsegmented stream of gestures.<br>by Peter M. Ju.<br>S.B.and M.Eng.
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Dempsey, George John. "Modelling the musculo-skeletal system using myoelectric signals." Thesis, University of Ulster, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329565.
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Roberts, Steven Michael. "Investigation into the control of an upper-limb myoelectric prosthesis." Thesis, University of Plymouth, 2002. http://hdl.handle.net/10026.1/505.
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Camargo, Daniel Rodrigues de. "Desenvolvimento do protótipo de uma prótese antropomórfica para membros superiores." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18151/tde-15102008-134653/.
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A finalidade desse trabalho é desenvolver um protótipo de uma prótese antropomórfica multifuncional para membros superiores para pacientes amputados. Seu objetivo é substituir a mão natural perdida, de forma a auxiliar a realização de algumas tarefas diárias do usuário. A prótese possuirá características antropomórficas, tais como aparência e movimentação semelhantes às da mão humana, e características naturais inerentes à mesma, por exemplo, o arco reflexo. Além disso, contará também com meios de realimentação táteis das informações de forças aplicadas pela prótese em objetos, bem como sua temperatura para o paciente, suprindo assim uma das carências das próteses convencionais. Esse dispositivo terá incorporado na sua construção sensores diversos para realizar as funções propostas e contará com um algoritmo baseado em redes neurais artificiais, capaz de identificar padrões dos sinais mioelétricos do paciente, que serão utilizados como sinais de controle, possibilitando ao paciente um comando natural. Todas essas implementações visam contribuir para a redução da taxa de rejeição de próteses para membros superiores e possibilitar uma maior reabilitação e reintegração do paciente à sociedade.<br>The purpose of this assignment is to develop a multifunctional and anthropomorphic upper limb prosthesis prototype for amputated patients. Its objective is to substitute the natural lost hand, in a way to improve the performance of regular activities. This prosthesis will have anthropomorphic characteristics, like appearance and movement, similar to the ones of the human hand, and natural characteristics inherent to it, for example the reflected arc. Another characteristic will be the tactile feedback ways of obtaining the information of the forces applied by the prosthesis in objects, as well as their temperature for the patient, overcoming therefore one of the traditional prosthesis\' deficiency. This device will have incorporated in its construction many sensors in order to do the proposed functions and it will use an algorithm based on the artificial neural network that is able to recognize patterns of myoelectric signals of the patient, which will be used as control signals, making possible to the patient a natural command. All of these implementations objective to contribute for the reduction of the rejection rate of prostheses for upper limb members and make possible a better rehabilitation and reintegration of the patient in the society.
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Day-Williams, Hugh C. "Effects of training methods on classification on surface electromyographic signals for myoelectric control." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119965.
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Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 25).<br>Myoelectric devices, devices which use the electric signals from human muscles as a control scheme, have shown promise in their potential to aid in human movement augmentation and assistance for those that have suffered injury. Previous studies involving myoelectric devices and the classification of surface electromyographic (sEMG) signals, electrical impulses obtained from muscles from sensors on the skin, have sought to use various types of machine learning models for sEMG pattern recognition. This technique shows promise in being able to accurately classify human sEMG signals and map them to certain movements, which can then be used as a method of myoelectric control. In this study we explored how two methods of training a K-Nearest Neighbor (KNN) classifier, used to control a MyoPro arm orthosis, affect two subjects' performance on various experimental tasks and their measured sEMG activation throughout the tasks. It was found that for subject 1, the assisted training method, where another individual helps move the orthosis while training the KNN, resulted in a lower variance in the measured mean sEMG values, and reduced the cross validation accuracy of the controller, but did not reduce subjects' performance of the experimental trials, as compared to the KNN controller trained without assistance. For subject 2, the assisted controller reduced the performance on three out of the four tests performed compared to the unassisted controller.<br>by Hugh C. Day-Williams.<br>S.B.
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Pachnis, I. "Neutralisation of myoelectric interference from recorded nerve signals using models of the electrode impedance." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/708399/.
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Any form of paralysis due to spinal cord injury or other medical condition, can have a significant impact on the quality and life expectancy of an individual. Advances in medicine and surgery have offered solutions that can improve the condition of a patient, however, most of the times an individual’s life does not dramatically improve. Implanted neuroprosthetic devices can partially restore the lost functionalities by means of functional electrical stimulation techniques. This involves applying patterns of electrical current pulses to innervate the neural pathways between the brain and the affected muscles/organs, while recording of neural information from peripheral nerves can be used as feedback to improve performance. Recording naturally occurring nerve signals via implanted electrodes attached to tripolar amplifier configurations is an approach that has been successfully used for obtaining desired information in non-acute preparations since the mid-70s. The neural signal (i.e. ENG), which can be exploited as feedback to another system (e.g. a stimulator), or simply extracted for further processing, is then intrinsically more reliable in comparison to signals obtained by artificial sensors. Sadly, neural recording of this type can be greatly compromised by myoelectric (i.e. EMG) interference, which is present at the neural interface and registered by the recording amplifier. Although current amplifier configurations reduce myoelectric interference this is suboptimal and therefore there is room for improvement. The main difficulty exists in the frequency-dependence of the electrode-tissue interface impedance which is complex. The simplistic Quasi-Tripole amplifier configuration does not allow for the complete removal of interference but it is the most power efficient because it uses only one instrumentation amplifier. Conversely, the True-Tripole and its developed automatic counterpart the Adaptive-Tripole, although minimise interference and provide means of compensating for the electrode asymmetries and changes that occur to the neural interface (e.g. due to tissue growth), they do not remove interference completely as the insignificant electrode impedance is still important. Additionally, removing interference apart from being dependent on the frequency of the interfering source, it is also subject to its proximity and orientation with respect to the recording electrodes, as this affects the field. Hence neutralisation with those two configurations, in reality, is not achieved in the entire bandwidth of the neural signal in the interfering spectrum. As both are less power efficient than the Quasi-Tripole an alternative configuration offering better performance in terms of interference neutralisation (i.e. frequency-independent, insensitive to the external interference fields) and, if possible, consume less power, is considered highly attractive. The motivation of this work is based on the following fact: as there are models that can mimic the frequency response of metal electrodes it should be possible, by constructing a network of an equivalent arrangement to the impedance of electrodes, to fit the characteristic neutralisation impedance – the impedance needed to balance a recording tripole – and ideally require no adjustment for removing interference. The validity of this postulation is proven in a series of in-vitro preparations using a modified version of the Quasi-Tripole made out of discrete circuit components where an impedance is placed at either side of the outer electrodes for balancing the recording arrangement. Various models were used in place of that impedance. In particular, representing the neutralisation impedance as a parallel RC reduced interference by a factor of 10 at all frequencies in the bandwidth of the neural signal while removed it completely at a spot frequency. Conversely, modelling the effect of the constant phase angle impedance of highly polarisable electrodes using a 20 stages non-uniform RC ladder network resulted in the minimisation of interference without the initial requirement of continuous adjustment. It is demonstrated that with a model that does not perfectly fit the impedance profile of a monopolar electrochemical cell an average reduction in interference of about 100 times is achieved, with the cell arranged as a Wheatstone bridge that can be balanced in the ENG band.
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Hákonardóttir, Stefanía. "Prosthetic Control using Implanted Electrode Signals." Thesis, KTH, Skolan för teknik och hälsa (STH), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147699.
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This report presents the design and manufacturing process of a bionic signal messagebroker (BSMB), intended to allow communication between implanted electrodes andprosthetic legs designed by Ossur. The BSMB processes and analyses the data intorelevant information to control the bionic device. The intention is to carry out eventdetection in the BSMB, where events in the muscle signal are matched to the events ofthe gait cycle (toe-o, stance, swing).The whole system is designed to detect muscle contraction via sensors implantedin residual muscles and transmit the signals wireless to a control unit that activatesassociated functions of a prosthetic leg. Two users, one transtibial and one transfemoral,underwent surgery in order to get electrodes implantable into their residual leg muscles.They are among the rst users in the world to get this kind of implanted sensors.A prototype of the BSMB was manufactured. The process took more time thanexpected, mainly due to the fact that it was decided to use a ball grid array (BGA)microprocessor in order to save space. That meant more complicated routing and higherstandards for the manufacturing of the board. The results of the event detection indicatethat the data from the implanted electrodes can be used in order to get sucient controlover prosthetic legs. These are positive ndings for users of prosthetic legs and shouldincrease their security and quality of life.It is important to keep in mind when the results of this report are evaluated that allthe testing carried out were only done on one user each.
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Heald, Elizabeth Ann. "Volitional Myoelectric Signals from the Lower Extremity in Human Cervical Spinal Cord Injury: Characterization and Application in Neuroprosthetic Control." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1575652691235174.
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Taffler, Sean. "The use of the Hilbert Spectrum in the analysis of electromyographic signals and its application in the development of myoelectric prosthesis controllers." Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393789.
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Bermudez, Rosa Maria Jimenez. "Proposta de um sistema baseado em redes neurais e wavelets para caracterização de movimentos do segmento mão-braço." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/179572.
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Este trabalho apresenta um sistema para o processamento do sinal mioelétrico baseado em Redes Neurais e Wavelets. Com a aquisição dos sinais mioelétricos dos músculos do segmento mão-braço, é possível determinar diversos parâmetros para a caracterização dos movimentos executados. A Transformada Wavelets foi utilizada na etapa de segmentação do sinal e a rede neural artificial na caracterização do movimento executado. O sistema é constituído de um eletromiógrafo (EMG de 8 canais), placa de aquisição de dados e um computador responsável pelo processamento dos dados. Foram utilizado eletrodos de superfície posicionados em lugares estratégicos no segmento mão-braço. O experimento consiste em repetir movimentos do segmento mão-braço executados por um modelo virtual. Os movimentos avaliados, neste trabalho, são: contração da mão, extensão do punho, flexão do antebraço, flexão do punho, rotação do braço, rotação e flexão do antebraço, rotação do braço e contração da mão, extensão e flexão do punho, contração da mão e elevação do braço. Esses movimentos são apresentados ao sujeito em determinadas sequências através dos modelos virtuais desenvolvidos, permitindo assim, a padronização do movimento a ser executado pelo voluntário O sinal é adquirido através de uma placa de aquisição e processado. As etapas principais de processamento são: segmentação do sinal de interesse através da Wavelet Discreta, extração de características (r.m.s, variância, desvio padrão, sesgo, curtose ) e uso da Rede Neural para determinar o movimento executado final dos testes, foi verificado que o movimento contração da mão e elevação do braço apresentou uma taxa de acerto média de 75%; o movimento flexão do antebraço obteve 81% de acerto médio; a contração da mão obteve 33% de acerto médio, o movimento contração da mão 76% de acerto médio; o movimento de flexão do punho 100 % de acerto médio, rotação e flexão do antebraço 66% de acerto médio, extensão e flexão do punho um 16% de acerto médio, extensão do punho 83,3% de acerto médio, rotação do braço 16,7% de acerto médio. Rotação do braço e contração da mão 83,3% de acerto médio.<br>This work presents a neural-network myoelectric processing-based system. With the acquisition of myoelectric signals from the muscles of the hand-arm segment, it is possible to determine the parameters that characterize the executed movements. Therefore, in this work Artificial Neural Networks are implemented to recognize patterns in order to determine the executed movement. The system is constituted by an electromyography (8-channel EMG), a data acquisition board and a computer responsible for data processing. In this research an experimental system is developed to capture the myoelectric signals by means of an EMG and a data acquisition board. Surface electrodes located in strategic places in the hand-arm segment are used. The experiment consists of repeated movements of the hand-arm segment executed by a virtual model. The movements examined in this work are: hand contraction, fist extension, forearm flexion, fist flexion, arm rotation, forearm rotation and flexion, fist contraction and extension and arm elevation. Those movements are presented to a volunteer in a random way by means of the virtual models developed, permitting a standardization of the movements that are to be executed by the volunteer. In the last part it is verified that the hand-contraction movement and the arm-elevation movement present an accuracy rate average of 75%; the forearm-flexion movement reaches 81% of accuracy rate average, the hand-contraction movement with 33% of accuracy rate average, the hand-contraction movement with 76% of accuracy rate average, the fist-flexion movement reached a 100% in the accuracy rate average, the forearm rotation flexion movement with a 66% in the accuracy rate average, the fist extension and flexion movement reaches the 16% in the accuracy rate average and the fist-extension movement with a 83.3% of accuracy rate average.
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Vidal, Tabata. "Concepção de proteses mioeletricos de membros superiores baseado no estudo fisiologico." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264737.
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Orientador: Helder Anibal Hermini<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica<br>Made available in DSpace on 2018-08-11T07:22:27Z (GMT). No. of bitstreams: 1
Vidal_Tabata_M.pdf: 5227439 bytes, checksum: 003d20540fa55512408c2b363171f23c (MD5)
Previous issue date: 2008<br>Resumo: O objetivo deste trabalho foi revisar na literatura os desenvolvimentos da aplicação das tecnologias envolvidas em automação dedicadas às próteses mioelétricas de membros superiores, desde os primeiros trabalhos implementados no período pós-Segunda Guerra Mundial até as soluções tecnológicas atualmente utilizadas. O conceito de prótese mioelétrica envolve a aquisição e tratamento do sinal mioelétrico de um membro residual que é usado para acionar um atuador que ativará a ferramenta terminal. Objetivando a elaboração de uma solução compatível e aplicável harmonicamente ao sistema orgânico, foram realizados estudos da anatomia, da fisiologia articular dos membros superiores, da natureza e características do sinal mioelétrico, além das tecnologias envolvidas para a concepção de protótipos mecatrônicos, tais como técnicas de CAD-CAE-CAM e a geração de circuitos eletrônicos dedicados à coleta e tratamento de sinais mioelétricos. Para validar o desenvolvimento teórico, três protótipos da ferramenta terminal foram confeccionados, sendo testados em nível de bancada<br>Abstract: The goal of this work was to review the specialized literature for the development of technological applications connected with automation of myoelectrical prosthesis of upper limbs throughout the years, from World War 2 post-war solutions to the technology currently applied. The concept of myoelectrical prosthesis presupposes implies the acquisition and treatment of the myoelectrical signal of a residual limb which is used to start an actuator, which in turn activates the terminal tool. Aiming at encountering a solution that could be both compatible and harmoniously applicable to the human body, the author engaged in studying anatomy, upper limbs articular physiology, the nature and characteristics of the myoelectrical signal in addition to the technologies utilized to conceive mechatronic prototypes, i.e. CAD-CAE-CAM and the creation of dedicated electronic circuits to collect and process the myoelectrical signals. To validate the theoretical foundation of this project, three prototypes of terminal tools were manufactured and bench-tested.<br>Mestrado<br>Mecanica dos Sólidos e Projeto Mecanico<br>Mestre em Engenharia Mecânica
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Fernandez, Jaime Julio. "Myoelectric signal recognition using genetic programming." Thesis, 1995. http://hdl.handle.net/1911/13948.
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This thesis presents a new method of myoelectric signal recognition. Myoelectric signals are electric signals generated by the motion of a person's muscle and can be used as control input for prosthetic hands. It uses genetic programming to create a set of equations capable of recognizing three different myoelectric signals.
Three different approaches are presented. The first approach uses genetic programming to create three separate equations. Each equation is capable of recognizing a different pair of the three myoelectric signals. The solution is accomplished by the signal that exactly corresponds to two of the three equations. The second approach creates a single equation capable of distinguishing the three signals. The last approach is a hybrid solution. It uses a simple equation to distinguish 90% of the three signals. It then uses a more complicated equation to distinguish the remaining 10% of the signals.
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Hofmann, David. "Myoelectric Signal Processing for Prosthesis Control." Doctoral thesis, 2014. http://hdl.handle.net/11858/00-1735-0000-0022-5DA2-9.
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Rehbaum, Hubertus. "Intuitive Myoelectric Control of Upper Limb Prostheses." Doctoral thesis, 2014. http://hdl.handle.net/11858/00-1735-0000-0022-5EA5-C.
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XIE, ZHI-HUI, and 謝志輝. "Autoregressive model and pattern recognition technique to upper limb myoelectric signal analysis." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/92525874344817735597.
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Tarng, Ying-Horng, and 唐瑩宏. "The Design of a Myoelectric Signal Controlled Human/Computer Interface for Human with Disability." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/78602259827429076141.
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碩士<br>國立臺灣大學<br>電機工程學系<br>85<br>A new human-computer interface(HCI) based on a real-time
electromyogram (EMG) recognition system is developed. A
personal computer with a plug-in data acquisition and
processing board(dSPACE DS1102) containing a TMS320
C31 floating-point digital signal processor are used to attain
real-time EMG classification. The recognition results are used
as control commands of the human-computer interface. This
system compatible with Microsoft mouse can move the cursor
in four directions and click the icon in GUI operating
systems.
Two channel EMG signals are collected by two pairs of surface
electrodes located on the sternocleidomastoid and the
upper trapezius, bilaterally. The integrated EMG is employed
to detect the onset of muscle contraction. The cepstral
coefficients, which are derived from autoregressive coefficients
and estimated by a recursive least square algorithm, are used
as the recognition feature. These features are then identified
using a modified maximum likelihood distance
classifier with a rejecting rule. They are successfully
implemented to be applied in the system.
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Martins, Henrique. "An Integrated Instrumentation Amplifier for Myoelectric Signals." Dissertação, 2013. http://hdl.handle.net/10216/73187.
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Martins, Henrique Rodrigues de Castro Mendes. "An Integrated Instrumentation Amplifier for Myoelectric Signals." Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/68040.
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Martins, Henrique Rodrigues de Castro Mendes. "An Integrated Instrumentation Amplifier for Myoelectric Signals." Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/68040.
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SHIAU, TSU-SHENG, and 蕭竹生. "Pattern analysis of myoelectrical signals as control signals." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/65365791673661833131.
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Hwang, Yuh Fa, and 黃裕發. "Autoregressive Model to Quadrcieps Femoris Isokinetic Myoelectric Signals Analysis." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/68818323891432882771.
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ZHONG, YAN-ZHEN, and 鍾言珍. "Autoregressive model to lower limb isokinetic myoelectric signals analysis." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/06936418749231072779.
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Andrews, ALEXANDER. "Finger Movement Classification Using Forearm EMG Signals." Thesis, 2008. http://hdl.handle.net/1974/1574.
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To a person with an upper limb amputation or congenital defect, a well-functioning prosthesis can open the door to many work and life opportunities. A fundamental component of many modern prostheses is the myoelectric control system, which uses the myoelectric signals from an individual's muscles to control prosthetic movements. Though much research has been done involving the myoelectric control of arm and gross hand movements, more dexterous finger control has not received the same attention. Consequently, the goal of this study was to determine an optimal approach to the myoelectric signal classification of a set of typing motions. Two different movement sets involving the fingers of the right hand were tested: one involving digits two through five (4F - "four finger"), and the other involving digits one and two (FT - "finger/thumb").
Myoelectric data were collected from the forearm muscles of twelve normally-limbed subjects as they performed a set of typing tasks. These data were then used to test a series of classification systems, each comprising a different combination of system element choices. The best classification system over all subjects and the best classification system for each subject were determined for both movement sets.
The optimal subject-specific classification systems yielded classification accuracies of 92.8 ± 2.7% for the 4F movement set and 93.6 ± 6.1% for the FT movement set, whereas the optimal overall classification systems yielded significantly lower performance (p<0.05): 89.6 ± 3.4% for the 4F movement set and 89.8 ± 8.5% for the FT movement set. No significant difference in classification accuracy was found between movement sets (p=0.802). A two-way repeated measures ANOVA (α=0.05) was used to determine both significance results.<br>Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-31 14:59:43.151
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Cheng, Hang-Shing, and 鄭恆星. "Improving Elbow Movement in Stroke Patients with External Torque Controlled by Myoelectric Signals." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/27648183646555137730.
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碩士<br>國立成功大學<br>機械工程學系<br>89<br>Stroke patients with upper motor unit lesion usually have observable muscle weakness in their affected side due to the abnormal efficiency of muscle contraction. The goal of this thesis is to enhance a stroke patient's muscle strength by adopting a control system which can actively provide the elbow joint with an appropriate torque based on EMG signals taken from triceps and biceps. By using this EMG controlled system the stroke patient's motor control capability for elbow joints can be improved and reduce the negative effects induced by muscle wealness.
Due to the discrepancy between contraction efficiencies of triceps and biceps, the ratio of unilateral EMG signals to elbow torque resulting from isometric contraction under various elbow angles are employed to construct a gain mapping matrix for system control. Co-activation within extensor and flexor can increase the stiffness of elbow joint and thus stabilize the motion of elbow. Therefore, in the control system, a nonlinear damping that has a physiological rationale is adopted to simulate the effect of co-activation. The coefficient of the nonlinear damping is determined by summing EMG signals of triceps and biceps. Since the wave form of control signals (i.e., EMG signals) resembles Gaussian distribution, the motor outputs a non-smooth torque trajectory to elbow joint which makes the subjects hard to accept the control system. Hence for obtaining a smooth torque trajectory, an adaptive filter is employed to automatically tune the bandwidth of the man-machine control system to within a permissible range.
Two sets of experiments are performed. In the first set the subjects are asked to move their forearm sgainst to a constant load from point to point while they follow a trajectory on the monitor. In the second set, the subjects are asked to perform a lift-hold-depose-hold movement against to a constant load too. Statistical analyses of the experiment results revealed the external torque can significantly improve the muscle power but cannot influence the tracking performance and nonlinear damping combined with the adaptive filter can stabilize the man-machine system and yield a much smoother movement.
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Chen, Han-Yu, and 陳翰裕. "The myoelectric signals of vastus medialis oblique and vastus lateralis at different knee flexion angles in normal subjects." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/43246682202558933753.
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碩士<br>國立臺灣大學<br>物理治療學研究所<br>88<br>The muscle imbalance of vustus medialis oblique (VMO) and vastus lateralis (VL) may produce excessive compression force on patellofemoral joint and is regarded to one of factors to cause patellofemoral pain syndrome (PFPS). Therefore, conservative treatment for the patients of PFPS is focused on strengthening of VMO. In the past, however, there were few studies to search for the relationship of VMO and VL as well as to compare the myoelectric signal of VMO and that of VL. The purposes of this study are to realize the difference between the myoelectric signals of VMO and that of VL, and the myoelectric signals ratio of those two, during isometric maximal voluntary contraction in different knee angles. Twenty-eight healthy students whose age from 20 to 30 were included. Subjects performed maximal isometric contraction providing resistance from Cybex dynamometer at 0°,15°,30°,45°,60°,75°,90°and 105°of knee flexion randomly. The myoelectric signals of VMO and VL were detected by surface electrodes, simultaneously as subjects did isometric contraction. The result of this study demonstrates the myoelectric signal amplitude of VMO and VL growing as greater as knee flexion increasing. The greatest myoelectric signals of VMO can be observed at knee flexion 105°while VL at flexion 90°.And the ratios of myoelectric signals of VMO to VL have no significant difference among the different knee flexion, except at flexion 45°. It is concluded that both VMO and VL, their myoelectric signal amplitude show growing as greater as knee flexion increasing comparably. And there is no significant difference between the myoelectric signal amplitude of VMO and that of VL for full range of knee motion.