Jali, M. H.; Izzuddin, T. A.; Bohari, Z. H.; Sarkawi, H.; Sulaima, M. F.; Baharom, M. F.; Bukhari, W. M.
July 2014
Journal of Engineering & Applied Sciences;Jul2014, Vol. 9 Issue 7, p1170
Academic Journal
Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffered with arm disability. The device is used to facilitate the tasks of the program and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (ANN) technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN) to model the EMG signal to torque value. The performance result of the network is measured based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.


Related Articles

  • Prediction of muscle activity during loaded movements of the upper limb. Tibold, Robert; Fuglevand, Andrew J. // Journal of NeuroEngineering & Rehabilitation (JNER);2015, Vol. 12 Issue 1, p132 

    Background Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex...

  • ART2 Neural Network for Surface EMG Decomposition. Xu, Zhengquan; Xiao, Shaojun; Chi, Zheru // Neural Computing & Applications;2001, Vol. 10 Issue 1 

    Extraction of individual Motor Unit Action Potentials (MUAPs) from a surface ElectroMyoGram (EMG) is an essential but challenging task for clinical study and physiological investigation. This paper presents an automatic decomposition of surface EMGs using a self-organised ART2 neural network. In...

  • Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. William Maner; Robert Garfield // Annals of Biomedical Engineering;Mar2007, Vol. 35 Issue 3, p465 

    AbstractObjective??To use artificial neural networks (ANNs) on uterine electromyography (EMG) data to classify term/preterm labor/non-labor pregnant patients.Materials And Methods??A total of 134 term and 51 preterm women (all ultimately delivered spontaneously) were included. Uterine EMG was...

  • Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Gang Wang; Zhizhong Wang; Weiting Chen; Jun Zhuang // Medical & Biological Engineering & Computing;Oct2006, Vol. 44 Issue 10, p865 

    In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we...

  • Myoelectric arm using artificial neural networks to reduce cognitive load of the user. Kutilek, Patrik; Mares, Jakub; Hybl, Jan; Socha, Vladimir; Schlenker, Jakub; Stefek, Alexandr // Neural Computing & Applications;Feb2017, Vol. 28 Issue 2, p419 

    Today's multiple degree-of-freedom myoelectric prosthesis relies only on direct control by the processed electromyographic signal. However, it is difficult for the wearer to learn unnatural muscle contractions in order to wield more than three DoFs of the arm. This makes it almost impossible to...

  • Man Versus Machine: What's the Standard? Lisco, Steven J. // Respiratory Care;Mar2011, Vol. 56 Issue 3, p350 

    The author reflects on the study by S. Bonnett and colleagues regarding a computerized advisory system to guide care of patients who need mechanical ventilation. He compares the study with other studies on computer-driven protocolized weaning. He discusses the reintroduction of the pressure...

  • Control of Neural Network Feedback Linearization Based on Chaotic Particle Swarm Optimization. Wang, S. X.; Li, H.; Li, Z. X. // Journal of Energy & Power Engineering;Apr2010, Vol. 4 Issue 4, p37 

    A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The...

  • DTC for Variable Speed Wind Turbine Driven Induction Generator Using ANN. Ananth, D. V. N.; Himabindhu, D.; Ramana, K. V.; Jagadeesh, V. // International Journal of Research & Reviews in Computer Science;Aug2011, Vol. 2 Issue 4, p959 

    In this paper, Artificial Neural Network based wind turbine with induction generator system is analyzed to develop constant voltage, ripple free stator flux, faster response of the machine with load torque and generator speed. In General wind speed varies with time, season, location etc....

  • Prediction of marine diesel engine performance by using artificial neural network model. Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Yasin, M. H. Mat; Ihsan, C. K.; Noor, M. M. // Journal of Mechanical Engineering & Science;Jun2016, Vol. 10 Issue 1, p1917 

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the output torque, brake power, brake specific fuel consumption and exhaust gas temperature. The input data for network training was gathered from engine laboratory testing running at various...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics