Design of sEMG-based clench force estimator in FPGA using artificial neural networks
- PDF / 1,405,665 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 18 Downloads / 192 Views
(0123456789().,-volV)(0123456789().,-volV)
S.I. : ADVANCES IN BIO-INSPIRED INTELLIGENT SYSTEMS
Design of sEMG-based clench force estimator in FPGA using artificial neural networks Sheikh Shanawaz Mostafa1,2,6
•
Md. Abdul Awal3,4
•
Mohiuddin Ahmad5
•
Fernando Morgado-Dias1,6
Received: 16 December 2017 / Accepted: 23 June 2018 The Natural Computing Applications Forum 2018
Abstract Hands are the main environmental manipulator for the human being. After losing a hand, the only alternative for the victim is to use a prosthesis. Despite the progress of science, the modern prosthesis has the same age-old problem of accurate force estimation. Among different kinds of force, clench force is the most important one. Because of this importance, this paper presents a hardware system that has been designed and implemented to estimate the desired clench force using surface Electromyography signals recorded from lower-arm muscles. The implementation includes a two-layer artificial neural network with a surface electromyography integrator. The neural network was trained with the Levenberg–Marquardt back propagation algorithm and was implemented in a field programmable gate array using an off-chip training method. The results from 10 datasets, recorded from five subjects, show that the hardware model is very accurate, with an average mean square error of 0.003. This suggests that the proposed design can mimic the behavior of clench force that a real limb does, and therefore this intelligent system could be a useful tool for any application related to prostheses. Keywords Artificial neural network Biomedical electronics Biomedical equipment Biomedical signal processing Field programmable gate arrays Prosthesis
1 Introduction In 2005, about 1.6 million people suffering from limb loss were living in the USA alone [1], and approximately 185,000 new amputations occurred yearly. The loss of a limb has a significant effect on the victim’s life, as arms are the main physical manipulator and such a loss will significantly affect manipulation ability. The percentage of
& Sheikh Shanawaz Mostafa [email protected] 1
Madeira Interactive Technologies Institute, Funchal, Portugal
2
Instituto Superior Te´cnico, Universidade de Lisboa, Lisbon, Portugal
3
Electronics and Communication Engineering Discipline, Khulna University, Khulna, Bangladesh
4
Signal Processing Research and Consultancy Group, The University of Queensland, Brisbane, QLD, Australia
5
Khulna University of Engineering and Technology, Khulna, Bangladesh
6
University of Madeira, Funchal, Portugal
upper limb (hand) amputation is 11% in the USA [2]. Consequently, these people are facing social neglect and are living a stressful life [3]. About 28.7% of amputees have a significant depressive syndrome and in 42% of the cases mental health services are needed [2]. Therefore, for the sake of these disabled people, state-of-the-art rehabilitation systems need to be built. However, a successful real-time implementation of robotic hand systems
Data Loading...
