Proceedings ArticleDOI
Optimal Selection Of Muscle Contraction States Using Probabilistic Neural Myoprocessor
T. Khoshaba,Kambiz Badie,Edmond Zahedi,R.M. Hashemi +3 more
- pp 488-489
TLDR
Results of simulating the network show the fact that reliable contraction states can be obtained for representing movement classes for both cases of two and three degree of freedom arm.Abstract:
In order to design an EMG-controlled cybernetic arm with high degree of freedom, the optimal subset of the possible muscle contraction states is to be determined for the purpose of controlling the arm. In this paper, optimal muscle contraction states have been selected using the classification error in probabilistic neural network (PNN). Results of simulating the network show the fact that reliable contraction states can be obtained for representing movement classes for both cases of two and three degree of freedom arm.read more
Citations
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Proceedings ArticleDOI
Improveci EMG pattern rocognition using polar segmentation of the feature space
TL;DR: Different segmentation schemes of the EMG feature space are considered in order to implement a myoelectric prosthetic control with a minimum classification error and it is shown that the use of polar coordinates instead of cartesian ones may lead to a lower misclassification rate.
A New Concept In The Evaluation Of Cybernetic Actuators Control Using Virtual Reality
Edmond Zahedi,Hitoshi Miyake +1 more
TL;DR: A new concept in developing a unique platform using virtual reality (VR) tools for evaluating both the different schemes of EMG signal processing and cybernetic control is introduced.
References
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Proceedings ArticleDOI
EMG Pattern Classification Based On Back Propagation Neural Network For Prosthesis Control
TL;DR: The application of a back propagation neural network to the classification of electromyogram (EMG) signal patterns for the control of prosthesis to show a high classification performance.