Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Bayesian approach for neural networks—review and case studies
Neural Networks
Detection and Estimation Methods for Biomedical Signals
Detection and Estimation Methods for Biomedical Signals
Time Series Analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computer Methods and Programs in Biomedicine
Electromyogram (EMG) driven system based virtual reality for prosthetic and rehabilitation devices
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Controlling a powered exoskeleton system via electromyographic signals
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Presence: Teleoperators and Virtual Environments
Comparison between MLP and LVQ neural networks for virtual upper limb prosthesis control
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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One of the major difficulties faced by those who are fitted with prosthetic devices is the great mental effort needed during the first stages of training. When working with myoelectric prosthesis, that effort increases dramatically. In this sense, the authors decided to devise a mechanism to help patients during the learning stages, without actually having to wear the prosthesis. The system is based on a real hardware and software for detecting and processing electromyografic (EMG) signals. The association of autoregressive (AR) models and a neural network is used for EMG pattern discrimination. The outputs of the neural network are then used to control the movements of a virtual prosthesis that mimics what the real limb should be doing. This strategy resulted in rates of success of 100% when discriminating EMG signals collected from the upper arm muscle groups. The results show a very easy-to-use system that can greatly reduce the duration of the training stages.