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.

本研究提出了一种基于多目标优化算法 NSGA-II 的监督学习框架，通过使用核 SVM 分类器对表面肌电信号（sEMG）进行解码以推断潜在肌肉运动，以设计一种节能的 EMG 控制器。实验结果表明，该方法为设计者在选择分类器参数以优化 EMG 控制器的能量效率方面提供了更高的灵活性。