• Corpus ID: 212496014

Recognition of sEMG for Prosthetic Control Using Static and Dynamic Neural Networks

@inproceedings{Emayavaramban2016RecognitionOS,
  title={Recognition of sEMG for Prosthetic Control Using Static and Dynamic Neural Networks},
  author={Emayavaramban},
  year={2016}
}
Several experiences were applied highlighting some great benefits of utilizing muscle sign in order to manage rehabilitation contraptions. This paper offers an investigating surface electromyography (sEMG) signal for classification of hand gestures to manipulate a prosthetic hand using neural networks. We assess the use of two channel surface electromyography to classify twelve person finger gestures for prosthetic control. sEMG alerts have been recorded from extensor digitorum and flexor… 
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References

SHOWING 1-10 OF 33 REFERENCES
Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography
TLDR
Assessment of the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control shows that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms.
FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL
TLDR
Time series analysis using Auto Regressive (AR) model and Mean frequency which is tolerant to white Gaussian noise are used as feature extraction techniques and EMG Histogram is used as another feature vector that was seen to give more distinct classification.
Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control.
TLDR
The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.
Forearm EMG Signal Classification Based on Singular Value Decomposition and Wavelet Packet Transform Features
TLDR
Results showed that proposed technique can achieve a classification recognition accuracy of over 96% for the eight hand motions and through quantitative comparison with other feature extraction methods like entropy concept, SVD method has a better performance.
PREDICTION OF ABOVE-ELBOW MOTIONS IN AMPUTEES, BASED ON ELECTROMYOGRAPHIC(EMG) SIGNALS, USING NONLINEAR AUTOREGRESSIVE EXOGENOUS (NARX) MODEL
TLDR
EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network, which seems to be suitable for dynamic system applications and has the potential to capture the model of nonlinear dynamic systems.
Decoding of Individuated Finger Movements Using Surface Electromyography
TLDR
It is shown that it is possible to decode individual flexion and extension movements of each finger with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals.
Proportional Myoelectric Control of a Multifunction Upper-limb Prosthesis
TLDR
To achieve better angle estimates that can be used for proportional control of prostheses, the aim was to use EMG signal features that are insensitive to amplitude changes due to variations in skin conductance.
EMG signal classification for human computer interaction: a review
TLDR
This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMg signal into computer command.
Hand Motion Classification Using a Multi-Channel Surface Electromyography Sensor
TLDR
Two new methods for feature extraction are proposed, which are robust and invariant to motion forces and speeds for the same gesture, and a new cascaded-structure classifier is proposed, in which 11 types of hand gestures can be identified accurately using the newly defined features.
A Study of Electromyogram Based on Human-Computer Interface
TLDR
A new control system based on forearm electromyogram (EMG) is proposed for computer peripheral control and artificial prosthesis control and the experiment result verifies that this control system can supply a high command recognition rate even the EMG data is collected with an EEG system just with single electrode measurement.
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