Abstract
The aim of this work is to classify EMG signals using a new neural network architecture to control multifunction prostheses. The control of these prostheses can be made using myoelectric signals taken from a single pair of surface electrodes. This case has been demonstrated specifically for use by above elbow amputees. The ability to separate different muscle contraction characters depends on myoelectric signal information. Therefore, the classification of these signals is investigated. The proposed neural network algorithm here makes the user learn better and faster.
Keywords
Artificial neural network
Conic section
Computer science
SIGNAL (programming language)
Pattern recognition (psychology)
Electromyography
Artificial intelligence
Neural Prosthesis
Speech recognition
Biomedical engineering
Engineering
Mathematics
Physical medicine and rehabilitation
Medicine
Subject Areas
Muscle activation and electromyography studies
·Biomedical Engineering ·Physical Sciences
EEG and Brain-Computer Interfaces
·Cognitive Neuroscience ·Life Sciences
Advanced Memory and Neural Computing
·Electrical and Electronic Engineering ·Physical Sciences
Citations by Year
2012: 2
2017: 1
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