Neuro-Musculoskeletal Mapping for Man-Machine Interfacing.

We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function

Comparison between low-cost and high-end sEMG sensors for the control of a transradial myoelectric prosthesis

Tese de mestrado integrado em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2017A amputação é algo pode mudar completamente a vida de qualquer indivíduo. A autonomia para executar tarefas do quotidiano, que a maioria de nós toma como garantidas, é drasticamente diminuída. Para além da dificuldade acrescida neste tipo de tarefas, a autoconfiança do individuo também sofre um duro decréscimo, podendo até originar situações de depressão. Por todas estas razões, a qualidade de vida de um amputado transradial é severamente afetada de forma negativa. Felizmente, atualmente já existem vários tipos de soluções prostéticas para tentar lidar com todos os obstáculos consequentes de uma amputação. Entre estas, encontram-se as próteses mioelétricas. Este tipo de próteses pode recorrer ao uso de algoritmos de reconhecimento de padrões para associar certos padrões observados em sinais de sEMG provenientes do coto a diferentes gestos de mão, oferecendo a possibilidade ao amputado transradial de restaurar alguma da sua autonomia utilizando um dispositivo com funcionalidades semelhantes à mão humana. Porém, existem alguns obstáculos relacionados com a acessibilidade destes dispositivos, mais especificamente, o preço. Atualmente, os preços das próteses mioelétricas comercialmente disponíveis são demasiados elevados, o que constitui um grande contratempo para indivíduos economicamente desfavorecidos que vivem com amputação transradial. Existe, portanto, a necessidade de diminuir os custos de produção e, consequentemente, o preço de mercado. No entanto, já existem alguns esforços a serem efetuados para tentar diminuir estes valores, tal como a impressão de algumas componentes em 3D. Para atingir este fim, também pode ser possível o uso de sensores de sEMG de baixo custo, ao invés de sensores sEMG de ponta. Porém, é necessário assegurar que a performance de controlo de uma prótese mioelétrica atingida pelo uso de sensores de baixo custo possa ser tão boa, ou superior à atingida pelo uso de sensores de ponta. Este é precisamente o grande foco desta dissertação. Para efetuar esta comparação, recorreu-se ao uso do Myo Armband e sensores da marca OttoBock. O Myo Armband é uma bracelete comercial de baixo custo que permite o controlo de aplicações multimédia e contém oito sensores de sEMG. Por outro lado, os sensores da OttoBock são os elétrodos de eleição para aplicações prostéticas. Estes dois tipos de sensores foram aplicados em dois sistemas sEMG distintos e duas experiências foram efetuadas de modo a avaliar a performance de cada um. Na primeira experiência foram efetuadas medições de sEMG nos antebraços de nove sujeitos saudáveis, com uso de ambos os sistemas. Foram usados diferentes algoritmos de reconhecimento de padrões para classificar segmentos do sinal sEMG correspondente a quatro gestos de mão diferentes. Em cada um dos sistemas foram usados cinco sensores. A experiência foi dividida em duas sessões. O protocolo seguido em cada uma das sessões foi exatamente o mesmo e a aquisição de dados foi realizada de forma contínua. Foi pedido a cada um dos sujeitos para visualizarem um vídeo e replicar cada um dos gestos mostrados neste mesmo. Cada um dos quatro gestos selecionados foi repetido 10 vezes, durante 10 segundos. Este procedimento foi repetido para cada um dos sistemas em cada uma das sessões. Embora cada gesto tenha sido registrado durante 10 segundos, apenas os últimos 6 segundos foram usados para classificação. Isto foi feito com o intuito de usar apenas o sinal de sEMG estável e não o transiente, que é originado pelo movimento do sujeito entre diferentes gestos. Diferentes técnicas de processamento de sinal e de extração de features foram aplicadas aos sinais adquiridos. Os dados obtidos, por sua vez, foram classificados por seis algoritmos diferentes, incluindo Linear Discriminant Analysis, Naïve Bayes, k Nearest Neighbours e três variações de Support Vector Machines. Esta experiência teve, portanto, o propósito de avaliar quais poderiam ser as combinações mais favoráveis entre diferentes técnicas de processamento de sinal e classificadores, de forma a obter a máxima precisão de classificação possível. Para avaliar as precisões calculadas, foram utilizados dois métodos de avaliação: 10-fold cross-validation e treino-teste. Os testes estatísticos efetuados aos resultados adquiridos demonstraram a inexistência de quaisquer diferenças significativas entre ambos os sistemas, o que valida a hipótese principal proposta por esta dissertação. No entanto, é necessário validar esta mesma hipótese com dados extraídos de amputados transradiais, os utilizadores finais deste tipo de sistemas. Na segunda experiência, as medições de sEMG foram efetuadas a doze amputados transradiais e a doze sujeitos saudáveis. Nesta experiência, em semelhança à primeira, também se realizaram duas sessões com protocolo igual. Contundo, comparativamente à experiência anterior, o protocolo usado sofreu algumas alterações. O número de sensores usados em cada um dos sistemas foi incrementado para oito e o número de gestos de mão foi aumentado para cinco. Os dados foram adquiridos de forma descontínua e a duração de cada aquisição realizada para cada gesto foi alterada para 2 segundos, de forma a obter apenas o sinal sEMG estável. Foram feitas 15 aquisições para cada um dos cinco gestos de mão, o que perfaz um total de 75 aquisições. As combinações de técnicas de processamento de sinal e classificadores usados nesta experiência foram selecionados de acordo com os resultados da primeira. No total, foram usadas quatro diferentes combinações de técnicas de processamento de sinal, retiradas das seis usadas na experiência anterior, e dois classificadores, uma das variações da Support Vector Machine e k Nearest Neighbours. As precisões calculadas voltaram a ser avaliadas novamente por meio de 10-fold cross-validation e de avaliação treino-teste. Os resultados obtidos demonstraram a inexistência de diferenças significativas entre as precisões adquiridas para cada um dos sistemas, exceto segundo os resultados da cross-validation. Neste caso, o sistema da OttoBock permitiu o cálculo de precisões superiores às obtidas pelo sistema da Myo Armband. Contundo, as precisões deste último demonstraram ser bastante competitivas. Nos resultados adquiridos, verificaram-se valores de precisão mais elevados no caso dos sujeitos saudáveis, em ambos os sistemas. Isto seria algo previsível, já que a não utilização diária do membro fantasma (a sensação de que membro amputado está ainda presente) leva a que o amputado se “esqueça” de como efetuava certos gestos com a mão que foi amputada. De um modo geral, pode-se afirmar que não se verificaram diferenças significativas entre os resultados obtidos em ambos os sistemas, o que valida a hipótese principal proposta por esta dissertação. De facto, os sensores de baixo custo usados permitiram resultados de classificação tão bons como os obtidos com o uso de sensores de ponta. Contudo, é de notar que isto é apenas possível com uso de algumas técnicas de processamento ao sinal aos dados obtidos pelos sensores da Myo, nomeadamente a aplicação de um envelope e de um filtro passa-baixo com uma frequência de corte de 1 Hz. Sem qualquer tipo de processamento, os resultados obtidos com estes sensores foram bastante fracos. Por outro lado, os sensores da OttoBock, mesmo sem qualquer tipo de processamento de sinal, permitiram resultados bastante elevados, o que se deve ao facto de produzirem um sinal previamente filtrado, com envelope e amplificado, ou seja, um sinal de alta qualidade. Considerando os resultados obtidos, é de facto possível que a aplicação de sensores de baixo custo a um sistema de controlo de uma prótese mioelétrica possa permitir uma performance tão boa como a oferecida por sensores de ponta. Contudo, isto é apenas possível se o processamento de sinal usado for apropriado, assim como o classificador escolhido. Em suma, é possível a substituição dos sensores atualmente usados em aplicações prostéticas por sensores com um custo mais reduzido, de modo a obter dispositivos mais económicos sem comprometer a qualidade do seu funcionamento. No entanto, antes destes sensores serem aplicados numa prótese mioelétrica, é necessário testar o sistema em tempo real e desenhar uma estratégia de controlo robusta, que permita uma boa comunicação entre as intenções do utilizador e as funcionalidades inerentes da prótese.The loss of a hand due to amputation can completely change anyone’s life. The autonomy to perform daily life tasks, which most of us take for granted, is drastically reduced, as well as one’s quality of life. Fortunately, the use of a myoelectric prosthesis can help in overcoming such problems a transradial amputee must face every day. However, the current cost of such devices can limit its accessibility to economically less favored people. In this dissertation, it is hypothesized that low-cost sensors can have a performance in controlling a myoelectric prosthesis as good as, or even better than the high-end sensors that are currently used in such applications. If this hypothesis can be validated, it may help in decreasing the costs of a myoelectric prosthesis and making it more accessible for the final user, the transradial amputee. To compare both types of sensors, two experimental sessions were performed. The first one was performed only on able-bodied subjects and it had the objective of selecting the best combination of signal processing techniques and classifiers in order to use on the obtained sEMG signals. In the second experiment, sEMG measurements were performed on both able-bodied and transradial amputated subjects. The signal processing techniques and classifiers that allowed to obtain the best results in the first experiment were used to classify the acquired data from all the subjects. Overall, the accuracies calculated with the usage of the low-cost sensors, using some of the signal processing techniques, proved not to be significantly different from the ones obtained with the usage of the high-end sensors. This indicates that the usage of low-cost sensors in systems to control a myoelectrical prosthesis might indeed provide a performance as efficient as high-end sensor. Besides, it may provide the possibility to lower the overall cost of the currently available devices

AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira

Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity

Investigation of wrist and hand function for the improvement of upper limb prosthetic device design

The development of upper limb prostheses faces many different challenges. Speciically, improvements in device design are urgently required, together with increased personalisation of devices according to patients’ needs and overall device ease of use and affordability. This investigation is part of a project entitled Anthropomorphic Design for Advanced Manufacturing (ADAM) which aims to develop a new Design System for personalisation of upper limb prosthetics using additive manufacturing technology. Here, a novel procedure for acquiring data on the movement features of the sound and prosthetic upper limb will be presented, as input for those design requirements

Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering

© 2001-2011 IEEE. Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy (> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications

Design and Analysis of a Myoelectric Arm Prosthesis for

Amputees struggle to function because of the large degree of dependence they need to execute basic tasks that people could normally do. Amputees usually opt to use a prosthesis, for cosmetic and other functional reasons, which are not often made for situations with intense physical exertion such as the workplace. Thus, this study aims to create a mechanical arm prosthesis design that is occupationally suitable for transradial amputees. The device is mostly made of acrylonitrile butadiene styrene (ABS), a type of thermoplastic. A digital model of the prosthesis, divided into three subassemblies, was created via Autodesk Inventor. These then went through Finite Element Analysis in which a 400 N load was placed to simulate a pushing force. After the simulations, it was proven that the individual subassemblies can withstand the specified force with minimal displacement and without yielding which shows that larger forces could be exerted. This also shows that ABS is a suitable material for creating such assistive devices. Further study could be made by optimizing the geometry and changing the orientation of the loads

Human-centered Electric Prosthetic (HELP) Hand

Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti, we designed a functional, robust, and and low cost electrically powered prosthetic hand that communicates with unilateral, transradial, urban Indian amputees through a biointerface. The device uses compliant tendon actuation, a small linear servo, and a wearable garment outfitted with flex sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The prosthesis was developed such that future groups can design for manufacturing and distribution in India

Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control.

We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods