Serious gaming to generate separated and consistent EMG patterns in pattern-recognition prosthesis control

Recent Advances in EMG Pattern Recognition for Prosthetic Control

A New Real-time Fuzzy Inference System for Emg Pattern Recognition Suitable for Hand Prosthesis Control

Electromyography (EMG) is the study of muscle function through the inquiry of the electrical signals that the muscles emanate. EMG signal collected form surface of the skin can be used in different applications such as recognizing patterns of hand prosthesis movements. We used intelligent approach based on artificial neural network (ANN) and Fuzzy inference system (FIS) with a real-time learnin...

Towards Robustness in Pattern Recognition Based Myoelectric Prosthesis Control

Commercial myoelectric prosthetic systems are based on relatively simple signal acquisition and processing. Academic research has indeed been developing powerful algorithms with promising results (e.g., pattern recognition methods and linear or non-linear transformations [1-4]), but there are still no commercial systems available using these approaches because they are not robust enough for cli...

Fuzzy EMG classification for prosthesis control.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classifica...

Serious gaming requires serious interfaces

This paper focuses on the application of intuitive game interfaces (including brain machine interfaces) to serious gaming, in particular real-time, interactive, and highly realistic environments for advanced concept development, experimentation and training.