Direct Estimation of Wrist Joint Angular Velocities from Surface EMGs by Using an SDNN Function Approximator
The present paper proposes a method for estimating joint angular velocities from multi-channel surface electromyogram (sEMG) signals. This method uses a selective desensitization neural network (SDNN) as a function approximator that learns the relation between integrated sEMG signals and instantaneous joint angular velocities. A comparison experiment with a Kalman filter model shows that this method can estimate wrist angular velocities in real time with high accuracy, especially during rapid motion.
KeywordsSurface electromyogram Angular velocity estimation Selective desensitization neural network
This work was supported partly by JSPS KAKENHI grant numbers 22300079 and 24700593 and by Tateishi Science and Technology Foundation grant number 2157011.
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