Atsushi Kiso

Robust motion discrimination based on human forearm myoelectric potential by adaptive fuzzy inference considering muscle fatigue

This paper describes a robust motion discrimination method based on the myoelectric potential of human forearm by the adaptive fuzzy inference considering the muscle fatigue. In the conventional studies, a motion discrimination based on the myoelectric potential of human forearm realizes the high discrimination precision. However, the characteristic of the myoelectric potential gradually changes for muscle fatigue. Therefore the motion discrimination considering muscle fatigue is required. The purpose of this study is to correspond to the change in the myoelectric potential by the muscle fatigue and keep the high discrimination precision. This study proposes the redesign method of the fuzzy inference adapting to the dynamic change of the myoelectric potential by the muscle fatigue. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

Optimal measurement position estimation of EMG signal by multi-regression analysis for human forearm motion discrimination

This paper describes an optimal measurement position estimation of the myoelectric signal by the multiple regression analysis for human forearm motion discrimination. The conventional studies use a lot of myoelectric sensors to obtain the high discrimination precision from the myoelectric signal. However, the use of a lot of myoelectric sensors becomes difficult by the cost and amputating situation of the human forearm. The purpose of this study is to decide the optimal measurement position for the motion discrimination, and to obtain high discrimination precision of the human forearm motion. This study proposes the selection method of the optimal measurement position to estimate the identification target motion from the myoelectric signal measured from multiple positions by the multiple regression analysis. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position selection method.

Human forearm motion discrimination based on myoelectric signal by fuzzy inference

This paper describes a human forearm motion discrimination method based on the myoelectric signal by the fuzzy inference. In the conventional studies, the neural network is often used to estimate motion intention by the myoelectric signal and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signal. This study designs the membership function and the fuzzy rules from the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

Disturbance road adaptive driving control of power-assisted wheelchair using fuzzy inference

This paper describes a novel driving control scheme of electric power-assisted wheelchairs for assistive driving on various large disturbance roads. The ”electric power-assisted wheelchair” which assists the driving force by electric motors is expected to be widely used as a mobility support system for elderly people and disabled people; however, there are lots of large disturbance roads such as uphill roads and rough roads and operators need to row the hand-rims with the larger power load on such roads in order to obtain the enough driving velocity. For example the wheelchair might move backward on uphill roads due to the driving torque shortage. Therefore this study proposes a fuzzy algorithm based adaptive control scheme in order to realize the assistive driving without the operators power load on large disturbance roads. The proposed fuzzy rules are designed from the driving distance information and the control parameters are inferred by the fuzzy algorithm. The assisted torque can be adjusted so that the enough distance and velocity are kept even on large disturbance roads. Driving experimental results are provided to verify the effectiveness of the proposed control system.