Abstract
Essential tremor is the most common of all involuntary movements. Many patients with upper limb tremor have serious difficulties performing daily activities. We developed a myoelectric controlled exoskeletal robot to suppress tremor. In this paper, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a low-pass filter (LPF) and neural network (NN) were used to recognize the tremor patient's movement. Using these techniques, it was difficult to recognize the movement accurately, because the myoelectric signal of the tremor patient periodically oscillated. Then, short-time Fourier transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm of using both short and long windows' STFTs, which is a kind of "mixture of experts", was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.
