EMG

— The Ionic Polymer Metal Composite (IPMC) actuator is a group of Electro-Active Polymer (EAP) which bends in response to a relatively low electrical voltage because of the motion of cations in the polymer network. IPMC has a wide range of applications in robotics, biomedical devices and artificial muscles. This paper presents a fuzzy logic approach to the electromyography (EMG) pattern recognition for an IPMC actuating system with the EMG signal. EMG signals generated by the contraction of muscles in the human forearm are used as an electrical stimulus for actuating the IPMC actuator. EMG is a method of recording and quantifying the electrical activity produced by the muscle fibers of the activated motor units. The fuzzy inference system is a system used to map an input feature onto an output class. Fuzzy data clustering is used to categorize the muscle signals and recognize the contraction of the muscle. The mechanical design matters such as light weight and small size with flexible behavior should also be considered. The IPMC has been vastly utilized as an artificial muscle because it can be constructed in a straight way and is driven by a relatively low input voltage. The EMG signal generated by the human flexor CarpiUlnaris muscle is pre-amplified before being transferred to the IPMC for achieving a large bending behavior. The experimental results confirm the ability of the IPMC to function as an artificial muscle actuated by the EMG signals

离子聚合物金属复合材料（IPMC）驱动器是一类电活性聚合物（EAP），在较低的电电压作用下，由于聚合物网络中阳离子的运动而弯曲。IPMC在机器人技术、生物医学设备和人工肌肉等领域具有广泛的应用。本文提出了一种模糊逻辑方法，用于基于EMG信号的IPMC驱动器的肌电图（EMG）模式识别。人类前臂肌肉收缩产生的肌电图信号被用作激活IPMC驱动器的电刺激。肌电图是一种记录和量化激活运动单位肌肉纤维产生的电活动的方法。模糊推理系统是一种将输入特征映射到输出类别的系统。模糊数据聚类用于对肌肉信号进行分类和识别肌肉的收缩。机械设计方面的因素，如轻量、小型和灵活的行为，也应予以考虑。IPMC因其可直线构建且由相对较低的输入电压驱动，已被广泛用作人工肌肉。人类屈指肌（CarpiUlnaris）产生的肌电图信号在传输到IPMC之前进行预放大，以实现较大的弯曲行为。实验结果证实了IPMC在由肌电图信号驱动的作为人工肌肉的功能。