Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

Title:Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

Volume: 14 Issue: 2

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

Affiliation:

• Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore,India

• Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore,India

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.

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.

Cite this article as:

Emayavaramban G.*, Amudha A.*, Rajendran T. , Sivaramkumar M., Balachandar K. and Ramesh T., Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks, Current Signal Transduction Therapy 2019; 14 (2) . https://dx.doi.org/10.2174/1574362413666180604100542