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