This paper describes the use of Fuzzy logic for the processing of EMG signals. This can increase the recognition rate and significantly reduce the number of computations required to generate an output. The initial placement of the Fuzzy sets was accomplished with the use of neural network techniques, these are not required for in the final system, only for setting up. The effectiveness of the features extracted from the EMG signals has been assessed using Principal Component Analysis (PCA) The developed system exhibits good generalisabilty but performs better when tuned to the intended user
Feature Extraction and Classification of Surface Electromyography (EMG) signals provide an access fo...
The main topics of research are in the sub-areas of neurophysiology that are concerned with measurem...
Real time controlling devices based on myoelectric singles (MES) is one of the challenging research ...
International audienceA technique is proposed that allows automatic decomposition of electromyograph...
The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipmen...
Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motio...
Electromyography (EMG) is obtained by measuring the electrical signal associated with the activatio...
Some electromyogram (EMG) signals include information from limb functions and have been used to cont...
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multi...
Electromyography (EMG) signals are an important technique in the control applications of prostatic h...
Electrocardiogram (ECG) and Phonocardiogram (PCG) signals embed key information about subject physio...
Sensors possess several properties of physical measures. Whether devices that convert a sensed signa...
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced ...
Pattern recognition using fuzzy logic and neural network techniques is usually related with image an...
In this paper, a discrimination system, using a neural network for electromyogram (EMG) externally c...
Feature Extraction and Classification of Surface Electromyography (EMG) signals provide an access fo...
The main topics of research are in the sub-areas of neurophysiology that are concerned with measurem...
Real time controlling devices based on myoelectric singles (MES) is one of the challenging research ...
International audienceA technique is proposed that allows automatic decomposition of electromyograph...
The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipmen...
Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motio...
Electromyography (EMG) is obtained by measuring the electrical signal associated with the activatio...
Some electromyogram (EMG) signals include information from limb functions and have been used to cont...
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multi...
Electromyography (EMG) signals are an important technique in the control applications of prostatic h...
Electrocardiogram (ECG) and Phonocardiogram (PCG) signals embed key information about subject physio...
Sensors possess several properties of physical measures. Whether devices that convert a sensed signa...
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced ...
Pattern recognition using fuzzy logic and neural network techniques is usually related with image an...
In this paper, a discrimination system, using a neural network for electromyogram (EMG) externally c...
Feature Extraction and Classification of Surface Electromyography (EMG) signals provide an access fo...
The main topics of research are in the sub-areas of neurophysiology that are concerned with measurem...
Real time controlling devices based on myoelectric singles (MES) is one of the challenging research ...