Özet:

Abstract The significance of human motion intentions in a designed exoskeleton wrist control hand is essential for stroke survivors, thus making EMG signals an integral part of the overall system is critically important. However, EMG is a nonlinear signal that is easily influenced by several errors from its surroundings and certain of its applications require close monitoring to provide decent outcomes. Hence, this paper proposes to establish the relationship between EMG signals and wrist joint angle to estimate the desired wrist velocity. Fuzzy logic has been selected to form a dynamic modelling of wrist movement for a single muscle at different MVC levels and double muscles at a similar MVC level. The physical model of the exoskeleton hand using Simmechanics Matlab software has been developed to validate the performance of the fuzzy logic output result from both dynamic modelling approaches. A PID controller has been developed to smooth the exoskeleton hand movement fluctuations caused by the fuzzy logic decision-making process. As a conclusion, results showed a strong relationship between EMG signals and wrist joint angle improved the estimation results of desired wrist velocity for both dynamic modelling approaches hence strengthened the prediction process by providing a myoelectronic control device for the exoskeleton hand.

Anahtar Kelimeler: