Abstract
Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to whom 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Louise Ada, S.D.C.G.C.: Strengthening interventions increase strength and improve activity after stroke: a systematic review. Aust. J. Physiotherapy 52, 241–248 (2006)
Adult Hemiplegia, B.B.: Evaluation and treatment. Butterworth-Heinemann, Oxford (1990)
Muthuswamy, J.: Biomedical Signal Analysis in Standard Handbook of Biomedical Engineering and Design, pp. 18.1–18.30. McGraw-Hill, New York (2004)
Li, D., Zhang, Y.: Artificial neural network prediction of angle based on surface electromyography. In: International Conference on Control, Automation and Systems Engineering (CASE), pp. 1–3 (2011)
Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F.: Techniques of EMG signal analysis: detection, processing, classification and applications. Biol. Proced. Online 8, 11–35 (2006)
Morita, S., Kondo, T., Ito, K.: Estimation of forearm movement from emg signal and application to prosthetic hand control. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 4, pp. 3692–36972 (2001)
Kent, L.M., Siegler, S., Guez, A., Freedman, W.: Modelling of muscle EMG to torque by the neural network model of back propagation. In: Proceedings of the Twelfth Annual IEEE International Conference of the Engineering in Medicine and Biology Society, pp. 1477–1478 (1990)
Naeem, U.J., Abdullah, A.A., Caihua X.: Estimating human arm’s muscle force using artificial neural network. In: Proceedings of IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6 (2012)
Favieiro, G.W., Balbinot, A., Barreto, M.M.G.: decoding arm movements by myoeletric signals and artificial neural networks. In: Conference of Biosignals and Biorobotics (BRC), pp. 1–6 (2011)
Jali, M.H., Sulaima, M.F., Izzuddin, T.A., Bukhari, W.M., Baharom, M.F.: Comparative study of EMG based joint torque estimation ANN models for arm rehabilitation device. Int. J. Appl. Eng. Res (IJAER) 9(10), 1289–1301 (2014)
Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O.: EMG motion pattern classification through design and optimization of neural network. In: International Conference on Biomedical Engineering (ICoBE), pp 175–179 (2012)
Hudgins, B., Parker, P., Scott, R.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993)
Mars, P., Chen J.R., Nambiar, R.: Learning Algorithms.: Theory and Applications in Signal Processing, Control and Communications. CRC Press , Boca Raton (1996)
Supeni, E.E., Epaarachchi, J.A., Islam, M.M., Lau, K.T.: Development of artificial neural network model in predicting performance of the smart wind turbine blade. In: 3rd Malaysian Postgraduate Conference (MPC 2013), pp. 4–5. Sydney, Australia (2013)
Acknowledgements
The authors would like to thanks Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Education, Malaysia for the financial supports given through Research Grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Jali, M.H. et al. (2015). Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-07674-4_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-07674-4_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07673-7
Online ISBN: 978-3-319-07674-4
eBook Packages: EngineeringEngineering (R0)