Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

Author(s): G. Emayavaramban*, A. Amudha*, T. Rajendran, M. Sivaramkumar, K. Balachandar, T. Ramesh.

Journal Name: Current Signal Transduction Therapy

Volume 14 , Issue 2 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network.

Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement.

Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models.

Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

Keywords: Surface electromyography, autoregressive, AR Burg, AR Yule Walker, AR Covariance, AR modified covariance levinson durbin recursion, linear prediction coefficient, cascade forward backpropagation neural network, pattern recognition neural network.

[1]
Gupta A, Vivekananda S. EMG myopathic signal detection using wavelet transform and neural network techniques. International Journal of Science and Advanced Technology 2012; 2(4): 107-11.
[2]
AlOmar F, Liu G. Analysis of extracted forearm sEMG signal using LDA, QDA, K-NN classification algorithms. Open Autom Control Syst J 2014; 6: 108-16.
[http://dx.doi.org/10.2174/1874444301406010108]
[3]
Ibrahimy MI, Ahsan MR, Khalifa OO. Design and performance analysis of artificial neural network for hand motion detection from EMG signals. World Appl Sci J 2013; 23(8): 751-8.
[4]
Gupta A, Vivekananda S. EMG myopathic signal detection using wavelet transform and neural network techniques. International Journal of Science and Advanced Technology 2012; 2(4): 107-11.
[5]
Al-Faiz MZ, Ahmed SH. Discriminant Analysis for human arm motion prediction and classifying. Intelligent Control and Automation 2013; 4(1): 26-31.
[http://dx.doi.org/10.4236/ica.2013.41004]
[6]
Fathia HA, Sundes BK, Mohamed KS, Mohamed El Gehani AA. The development of body-powered prosthetic hand controlled by EMG signals using DSP processor with virtual prosthesis implementation. Hindawi Publishing Corporation Conference Papers in Engineering 2013; p. 1.
[7]
Emayavaramban. G, Amudha. A, Ramkumar. S, Sathesh Kumar K. Classification of hand gestures using FFNN and TDNN networks. Int J Pure Appl Math 2018; 118(8): 27-32.
[8]
Emayavaramban G, Amudha A. Identifying hand gestures using sEMG for human machine interaction. J Eng Appl Sci (Asian Res Publ Netw) 2016; 11(21): 12777-85.
[9]
Ramkumar S. Sathesh Kumar K, Emayavaramban G, EOG signal classification using neural network for human computer interaction. International Journal of Control Theory and Applications 2016; 2(6): 173-81.
[10]
Ramkumar S, Emayavaramban G, Elakkiya A. A web usage mining framework for mining evolving user profiles in dynamic web sites. Int J Adv Res Comput Sci Softw Eng 2014; 4(8): 889-94.
[11]
Ramkumar S, Elakkiya A, Emayavaramban G. Kind data transfer model - tracking and identification of data files using clustering algorithms. International Journal of Latest Technology in Engineering 2014; 3(8): 13-21.
[12]
Elakkiya A, Ramkumar S, Emayavaramban G. Performance evaluation of mobile sensor network. Journal of Applied Engineering 2014; 2(8): 151-5.
[13]
Emayavaramban G, Amudha A. sEMG based classification of hand gestures using artificial neural networks. Indian J Sci Technol 2016; 9(35): 1-10.
[http://dx.doi.org/10.17485/ijst/2016/v9i35/96501]
[14]
Emayavaramban G, Amudha A. Recognition of sEMG for prosthetic control using static and dynamic neural networks. International Journal of Control Theory and Applications 2016; 9(24): 205-15.
[15]
Ramkumar S, Kumar KS, Emayavaramban G. A feasibility study on eye movements using electrooculogram based HCI. International Conference on Intelligent Sustainable Systems (ICISS) 2017.
[16]
Sathesh Kumar K. Nine States HCI using electrooculogram and neural networks. IACSIT Int J Eng Technol 2017; 8(6): 3056-64.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 14
ISSUE: 2
Year: 2019
Page: [158 - 164]
Pages: 7
DOI: 10.2174/1574362413666180604100542

Article Metrics

PDF: 9
HTML: 3

Special-new-year-discount