Myoelectric pattern recognition is one of the important aspects in the design of the control strategy for various applications including upper-limb prostheses and bio-robotic hand movement systems. The current work has proposed an approach to design an energy-efficient EMG-based controller by considering a kernelized SVM classifier for decoding the information of surface electromyography (sEMG) signals to infer the underlying muscle movements. In order to achieve the optimized performance of the EMG-based controller, our main strategy of classifier design is to reduce the false movements of the overall system (when the EMG-based controller is at the `Rest' position). To this end, we have formulated the training algorithm of the proposed supervised learning system as a general constrained multi-objective optimization problem. An elitist multi-objective evolutionary algorithm $-$ the non-dominated sorting genetic algorithm II (NSGA-II) has been used to tune the hyperparameters of SVM. We have presented the experimental results by performing the experiments on a dataset consisting of the sEMG signals collected from eleven subjects at five different upper limb positions. Furthermore, the performance of the trained models based on the two-objective metrics, namely classification accuracy, and false-negative have been evaluated on two different test sets to examine the generalization capability of the proposed training approach while implementing limb-position invariant EMG classification. It is evident from the presented result that the proposed approach provides much more flexibility to the designer in selecting the parameters of the classifier to optimize the energy efficiency of the EMG-based controller.

翻译：肌电模式识别是设计上肢假肢和生物机械手运动系统等多种应用控制策略的重要环节之一。本研究提出了一种通过考虑核化支持向量机（SVM）分类器来解码表面肌电（sEMG）信号信息，从而推断潜在肌肉运动的方法，以设计节能型肌电（EMG）控制器。为了实现EMG控制器的最优性能，我们分类器设计的主要策略是减少整个系统的误动作（当EMG控制器处于“静止”位置时）。为此，我们将所提出的监督学习系统的训练算法构建为一般性的带约束多目标优化问题。采用精英多目标进化算法——非支配排序遗传算法II（NSGA-II）来调整SVM的超参数。我们通过在包含十一位受试者在五种不同上肢位置采集的sEMG信号数据集上进行实验，展示了实验结果。此外，基于分类准确率和假阴性这两个目标指标，我们在两个不同的测试集上评估了训练模型的性能，以检验所提训练方法在实现肢体位置不变的EMG分类时的泛化能力。从展示的结果可以明显看出，所提出的方法为设计者在选择分类器参数以优化EMG控制器的能效方面提供了更大的灵活性。