Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines

10.1186/s12984-016-0183-0Journal of NeuroEngineering and Rehabilitation13118

Myoelectric Human Computer Interaction Using Reliable Temporal Sequence-based Myoelectric Classification for Dynamic Hand Gestures

To put a computerized device under human control, various interface techniques have been commonly studied in the realm of Human Computer Interaction (HCI) design. What this dissertation focuses on is a myoelectric interface, which controls a device via neuromuscular electrical signals. Myoelectric interface has advanced by recognizing repeated patterns of the signal (pattern recognition-based myoelectric classification). However, when the myoelectric classification is used to extract multiple discrete states within limited muscle sites, there are robustness issues due to external conditions: limb position changes, electrode shifts, and skin condition changes. Examined in this dissertation is the robustness issue, or drop in the performance of the myoelectric classification when the limb position varies from the position where the system was trained. Two research goals outlined in this dissertation are to increase reliability of myoelectric system and to build a myoelectric HCI to manipulate a 6-DOF robot arm with a 1-DOF gripper. To tackle the robustness issue, the proposed method uses dynamic motions which change their poses and configuration over time. The method assumes that using dynamic motions is more reliable, vis-a-vis the robustness issues, than using static motions. The robustness of the method is evaluated by choosing the training sets and validation sets at different limb positions. Next, an HCI system manipulating a 6-DOF robot arm with a 1-DOF gripper is introduced. The HCI system includes an inertia measurement unit to measure the limb orientation, as well as EMG sensors to acquire muscle force and to classify dynamic motions. Muscle force and the orientation of a forearm are used to generate velocity commands. Classified dynamic motions are used to change the manipulation modes. The performance of the myoelectric interface is measured in terms of real-time classification accuracy, path efficiency, and time-related measures. In conclusion, this dissertation proposes a reliable myoelectric classification and develops a myoelectric interface using the proposed classification method for an HCI application. The robustness of the proposed myoelectric classification is verified as compared to previous myoelectric classification approaches. The usability of the developed myoelectric interface is compared to a well-known interface

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

Métodos de classificação confiável e resiliente de movimentos de membros superiores baseado em extreme learning machines e sinais de eletromiografia de superfície

Apesar de avanços recentes, a classificação confiável de sinais de eletromiografia de superfície (sEMG) permanece uma tarefa árdua sob a perspectiva de Aprendizagem de Máquina. Sinais de sEMG possuem uma sobreposição de classes inerente à sua natureza, o que impede a separação perfeita das amostras e produz ruídos de classificação. Alternativas ao problema geralmente baseiam-se na filtragem do sEMG ou métodos de pós-processamento como o Major-Voting, soluções estas que necessariamente geram atrasos na classificação do sinal e frequentemente não geram melhoras substanciais. A abordagem deste trabalho baseia-se no desenvolvimento de métodos confiáveis e resilientes sob a perspectiva de classificação que gerem saídas mais estáveis e consistentes para o classificador baseado em Extreme Learning Machines (ELM) utilizado. Para tanto, métodos envolvendo o pré-processamento e pós-processamento, a suavização do arg max do classificador, thresholds adaptativos e um classificador binário auxiliar foram utilizados. Os sinais classificados derivam de 12 canais de sEMG envolvendo três bases de dados diferentes onde 99 ensaios compostos pela execução de 17 movimentos distintos do segmento mão-braço foram realizados. Nos melhores resultados, os métodos utilizados atingiram taxas de acerto médio global de 66,99 ± 23,6% para a base de voluntários amputados, 87,10 ± 5,89% para a base de voluntários não-amputados e taxas superiores a 99% para todas as variações de diferentes ensaios que compõe a base de dados adquirida em laboratório. Já para a taxa de acerto média ponderada por classes, nos melhores resultados foram de 53,36 ± 18,2% para a base de voluntários amputados, 77,94 ± 6,22% para a base de voluntários não-amputados e taxas superiores a 91% para os ensaios da base de dados adquirida em laboratório. Ambas as métricas de taxa de acerto consideradas superam ou equivalem-se a alternativas descritas na literatura, utilizando abordagens que não demandam grandes mudanças estruturais no classificador.Despite recent advances, reliable classification of surface electromyography (sEMG) signals remains an arduous task from the perspective of Machine Learning. sEMG signals have inherent class overlaps that prevent optimal labeling due to classification noises. Alternatives to classification ripples usually rely on stochastic sEMG filtering or post-processing methods, like Major-Voting, both solutions that insert constraints and additional delays in signal classification and often do not generate substantial improvements. The approach of this paper focuses on the development of reliable and resilient methods used in combination with an Extreme Learning Machines (ELM) classifier to generate more stable and consistent outputs. Methods of pre-processing and post-processing, a smoothed arg max version of the ELM, adaptive thresholds, and an auxiliary binary classifier were used to process signals derived from 12 EMG channels from three different databases. In total, 99 trials were performed, each one containing 17 different upper-limb movements. The proposed methods reached an average overall accuracy rate of 66.99 ± 23.6% for the amputee individuals’ database, 87.10 ± 5.89% for the non-amputee individuals’ database, and rates over 99% for all variations of our own lab-generated database. The average weighted accuracy rates were 53.36 ± 18.2% for the amputee individuals’ database, 77.94 ± 6.22% for the base of the non-amputee individuals’ database, and higher than 91% for the best-case scenario of our own lab-generated database. In both metrics considered, the results outperform, or match alternatives described in the literature using approaches that do not require significant changes in the classifier's architecture