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 supervised learning framework using 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, unlike the traditional single training objective of soft margin kernelized SVM, 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 for the tuning of SVM hyperparameters. 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. 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：肌电模式识别是上肢假肢及生物机器人手部运动系统等多种应用控制策略设计中的重要方面。本研究提出了一种实现低功耗肌电控制器的方法，采用基于核化支持向量机分类器的监督学习框架，解码表面肌电信号中的信息以推断底层肌肉运动。为优化肌电控制器的性能，我们的分类器设计主要策略是减少系统整体（当肌电控制器处于“静止”位置时）的误动作。为此，与传统软间隔核化支持向量机单一训练目标不同，我们将所提监督学习系统的训练算法构建为通用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进行支持向量机超参数调优。通过在包含11名受试者在五种不同上肢位置采集的表面肌电信号数据集上进行实验，结果表明：所提方法为设计者在选择分类器参数以优化肌电控制器能效方面提供了更灵活的方案。