Surface Electromyography (EMG) has been considered as a viable human-machine interface in the context of human-centered robotics. In order to interpret human muscle activities into motion intentions, various pattern classification methods was proposed for human motion/gesture classification, which provided binary command for myoelectric control. To obtain complex motions coordinated by multiple DoFs, single DoF was usually sequentially classified and activated, which is not intuitive and efficient comparing with the natural motor strategy of the human. In this work, we investigated the motion classification methods from EMG for intuitive and simultaneous activation of multiple DoFs during 3-D arm motions. In the experiments, all motions were performed naturally rather than under the condition of maximum muscle contractions or other kinematic constraints. The combination of two EMG time-domain features after principal component analysis (PCA) processing is considered as the suitable choice considering both the classification accuracy and feasibility for robot control. For the motion classification method, least-square support vector machine (LS-SVM) represents higher classification accuracy for five arm motion classification across eight subjects with respect to other four methods which were popularly used in the previous works. The proposed method is hopefully applied in a EMG-driven simultaneous and proportional kinematics estimation systems for decoding model selection according to the motion intention.
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机译：表面肌电图（EMG）被认为是人以人为本的机器人的背景下的可行的人机界面。为了将人体肌肉活动解释为运动意图，提出了各种模式分类方法，用于人类运动/手势分类，为肌电控制提供了二进制命令。为了获得由多种DOF协调的复杂运动，通常依次分类和激活单独的DOF，这与人类的天然电机策略相比并不直观和有效。在这项工作中，我们调查了从EMG的运动分类方法直观，同时激活了三维臂运动期间多方DOF。在实验中，所有运动都是自然的而不是在最大肌肉收缩或其他运动约束的条件下进行。 The combination of two EMG time-domain features after principal component analysis (PCA) processing is considered as the suitable choice considering both the classification accuracy and feasibility for robot control.对于运动分类方法，最小二乘支持向量机（LS-SVM）表示跨越八个受试者的五个臂运动分类的较高分类精度，相对于以前的工作中普遍使用的其他四种方法。所提出的方法希望根据运动意图解码模型选择的EMG驱动的同时和比例的运动学估计系统。
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