Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, 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 for 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.

This investigation was made in order to find out a distribution of residual stress on the surfaces ground under various grinding conditions obtained by changing an actual depth of cut, number of grinding and a grinding process under a constant state of a chip cross sectional area. The surfaces of the specimens were ground by only up cut, only down cut and both up and down cut, respectively. The experimental data were analyzed by using both the Dolle-Hauk method and the integral method which takes into account of the stress gradient.As a result, the residual stress on the surface of one pass grinding showed higher tensile stress when a larger actual depth of cut took place. And the residual stress of down cut grinding was higher than that of up cut. By repeating another re-grinding on the same surface, the residual stress translated into a compressive stress and then converged into a certain level. On the contrary, the stress gradient changed to positive side in such case.

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