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Abstract:   (189 Views)
In this study a method Has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of  sEMG signals and the force produced by fingers, 9 Requested  movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and Generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R2 Criterion. The Total value of R2  for 6 DoF was 92/8±5/2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous Myo-electric control.
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Type of Study: Research Paper | Subject: Biomedical Signal Processing
Received: 2018/01/07 | Accepted: 2018/05/24 | Published: 2018/05/24