Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals

@article{Tsenov2006NeuralNF,
  title={Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals},
  author={Georgi T. Tsenov and A. H. Zeghbib and Frank Palis and N. Shoylev and V. M. Mladenov},
  journal={2006 8th Seminar on Neural Network Applications in Electrical Engineering},
  year={2006},
  pages={167-171}
}
Myoelectric signals (MES) are the electrical manifestation of muscular contractions and they can be used to create myoelectric prosthesis which is able to function with the amputee's muscle movements. This signal recorded at the surface of the skin of the forearm has been exploited to provide recognition of the movement of the hand and finger movements of healthy subject. The objective of the paper is to describe the identification procedure, based on EMG patterns of forearm activity using… CONTINUE READING
Highly Cited
This paper has 68 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 43 extracted citations

Myoelectric control systems for hand rehabilitation device: A review

2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) • 2017
View 10 Excerpts
Highly Influenced

Closed-Loop Continuous Hand Control via Chronic Recording of Regenerative Peripheral Nerve Interfaces

IEEE Transactions on Neural Systems and Rehabilitation Engineering • 2018
View 1 Excerpt

Hand gesture movement classification based on dynamically structured neural network

2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) • 2018
View 2 Excerpts

Improved Gesture Recognition Using Deep Neural Networks on sEMG

2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST) • 2018
View 1 Excerpt

AugAuth: Shoulder-surfing resistant authentication for augmented reality

2017 IEEE International Conference on Communications (ICC) • 2017
View 2 Excerpts

EMG Spectral Analysis for Prosthetic Finger Control

2017 European Conference on Electrical Engineering and Computer Science (EECS) • 2017
View 1 Excerpt

69 Citations

051015'10'13'16'19
Citations per Year
Semantic Scholar estimates that this publication has 69 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 13 references

Self organization and associate memory

T. Kohonen
1989
View 6 Excerpts
Highly Influenced

Fuzzy systems and Neural Networks methods to identify hand and finger movements using surface EMG signals

A. H. Zeghbib, F. Palis, G. Tsenov, N. Shoylev, V. Mladenov
9-th WSEAS CSCC Multiconference Vouliagami, • 2005

Performance of surface EMG signals identification using intelligent computational methods

A. H. Zeghbib, F. Palis, G. Tsenov, N. Shoylev, V. Mladenov
WSEAS Transactions on Systems, Issue7, • 2005

A wevelet based continuous classification scheme for multifunction myoelectric control

K.Englehart, B. Hudgin, P. A. Parker
IEEE Transactions on Biomedical Engineering, • 2001
View 1 Excerpt

Automatic tuning of myoelectric prostheses.

Journal of rehabilitation research and development • 1998
View 1 Excerpt

Neural Networks: A Comprehensive Foundation, (2nd Edition 1998), ISBN: 0132733501

S. Haykin
1998
View 1 Excerpt

T-S, "Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface

Chang G-C, Kang W-J, +4 authors Kuo
Med Eng Phys • 1996
View 1 Excerpt

EMG feature evaluation for movement control of Upper extremity prostheses

Z. K. Mahyar, W. C. Bruce, B. Kambiz, M. H. Reza
IEEE Transactions on Rehabilitation Engineering, • 1995
View 1 Excerpt

EMG feature evaluation for movement control of Upper extremity prostheses "

Z. K. Mahyar, W. C. Bruce, B. Kambiz, M. H. Reza
1995

Similar Papers

Loading similar papers…