Investigation of muscle synergies as a real-time control strategy for myoelectric control of upper limb protheses

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University of New Brunswick


Electromyogram (EMG) pattern recognition has long been used for the control of powered upper limb prostheses by many researchers. However, several factors such as complexity of motions and variation of applied force challenge the robustness of pattern recognition control in practical use. Such challenging factors must be accommodated to yield truly robust performance of myoelectric control in real task oriented use. Motivated by the growing need to add functionality to current commercially available myoelectric prostheses, the current study is focused on helping with improvement of prosthetic device control toward being intuitive. The novel contribution of this research is the development and test of a platform and control algorithms based on a concept called muscle synergies which was initially introduced to explain the control strategy of the central nervous system (CNS) for coordinating muscles during motions. Based on the physiological attributes muscle synergies, the current research uses this concept toward control of prosthetic devices in a more natural feeling and physiologically expected manner. One important factor in the proportional control of prostheses is to estimate the level of muscle activity produced by the user performing the tasks. Our first study was to investigate the ability of synergies in estimating the produced force with the goal of using this estimation toward proportional control. For this aim, a regression control was performed in which the output was explicitly a single or multi-DOF estimate of force. The extracted muscle synergies demonstrated high repeatability for different repetitions of the same tasks and were quite robust across different force levels. The results indicated that muscle synergies are an effective representation of EMG in force estimation of multi-DoF tasks. Our observations strengthened the idea that predicting the forces produced in unknown levels can be possible by training the model with synergies. Also, it supported the idea that the synergies might be resilient to force changes to some extent. Evaluating their ability of force estimation in an offline test, synergies outperformed MAVs. However, a real-time control test reported no significant difference between the performance of synergies and MAVs. In an attempt to understand the dynamics of synergies, they were used as features of a pattern recognition based task classification. Also the effect of several factors such as force variation, complexity of tasks, features that synergies are extracted from, and the number of EMG channels, on the performance of synergies were examined. In general, relatively low classification errors were yielded by synergies. However, other than the cases with relatively large number of channels and synergies, the study showed that synergies’ performance was generally lagged behind that of TD features. As the performance of the proposed classification model basically depends on the choice of features and synergies, methods of producing more reliable and robust synergies were also investigated. Moreover, alternative heuristic methods were explored in an attempt to improve the synergy results outside of straightforward pattern recognition methods. Accordingly, the final study addresses the training issues and explores the classifier architecture issues. To mitigate the training issues and to improve the consistency of extracted synergies, three strategies were tested: using a validation set to select synergies, increasing the training data size, and constraining the solution space for synergy extraction method. Although, all three methods improved the results achieved by synergies, in all cases TD features still showed better performance than synergies. To explore the classifier architecture issues, strategies such as pooling synergies with TD features and extracting task specific synergies were tested. Both strategies significantly improved the previously achieved results.