Electromyography Control of a Computer Model of the Arm

Amad M. Alasker

Institute of Science and Technology in Medicine, Keele University, Keele, UK.

DOI: 10.4236/jbise.2014.713101                

Abstract

Human arm movements may be adversely affected in the event of stroke or spinal cord injuries, eventually causing the patient to lose control of arm movements. Electromyography (EMG) is con-sidered the most effective technique for the restoration of arm movement in such cases. The reha-bilitation period for such patients is usually long. Moreover, complex treatment techniques may demoralize them. Therefore, this study, attempts to contribute to the development of a relaxing rehabilitation environment through electromyography control of a computer model of the arm. The model is created using MATLAB? and Data LINK software and other requisite components for training the targeted participants to control their arm movements. Six male participants with no history of injury to the arms or back were selected using the set protocol. The results and data collected are analysed using three performance measures i.e. the number of target hits, average time to target, and path efficiency for each target. Then, the main results in terms of the obtained performance measures are discussed and compared with those of previous studies.

Keywords

Electromyography (EMG) Signals, Human Arm Movements, Muscle Control, Human Arm Model

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Shalaby, R.E.-S. (2011) Development of an Electromyography Detection System for the Control of Functional Electrical Stimulation in Neurological Rehabilitation. TU Berlin, 8-11.
[2]	Day, S. (2008) Important Factors in Surface EMG Measurement. Bortec Biomedical, 3-6.
[3]	Konrad, P. (2005) The ABC of EMG. Noraxon Inc., USA, 4-37.
[4]	Liu, J., Ni, L. and Ferri, M. (2011) ECE532: EMG Controlled Video Game. The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, 1-2.
[5]	Khezri, M. and Jahed, M. (2007) Real-Time Intelligent Pattern Recognition Algorithm for Surface EMG Signals. BioMedical Engineering Online, 6, 2. http://dx.doi.org/10.1186/1475-925X-6-45
[6]	Biometrics Limited (2013) DataLINK DLK900. http://www.biometricsltd.com/datalink.htm
[7]	Biometrics Limited (2013) Surface EMG. http://www.biometricsltd.com/semg.htm
[8]	Biometrics Limited (2013) Analysis Software. http://www.biometricsltd.com/analysis.htm
[9]	Williams, M.R. and Kirsch, R.F. (2008) Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human-Computer Interface for Individuals with High Tetraplegia. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16, 1-10. http://dx.doi.org/10.1109/TNSRE.2008.2006216
[10]	Fan, J., He, J.P. and Helms Tillery, S.I. (2006) Control of Hand Orientation and Arm Movement during Reach and Grasp. Experimental Brain Research, 171, 283-296. http://dx.doi.org/10.1007/s00221-005-0277-6
[11]	Amirabdollahian, F., Loureiro, R. and Harwin, W. (2002) A Case Study on the Effects of a Haptic Interface on Human Arm Movements with Implications for Rehabilitation Robotics. Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2002), Washington DC, 17-20.
[12]	Chadwick, E.K., Blana, D., Simeral, J.D., Lambrecht, J., Kim, S.P., Cornwell, A.S., Taylor, D.M., Hochberg, L.R., Donoghue, J.P. and Kirsch, R.F. (2011) Continuous Neuronal Ensemble Control of Simulated Arm Reaching by a Human with Tetraplegia. Journal of Neural Engineering, 8, 6. http://dx.doi.org/10.1088/1741-2560/8/3/034003
[13]	Thelen, E. (1996) A Dynamic Systems Approach to the Development of Cognition and Action. MIT Press, Cambridge, 215.
[14]	Liarokapis, M.V., Artemiadis, P.K., Katsiaris, P.T. and Kyriakopoulos, K.J. (2012) Learning Task-Specific Models for Reach to Grasp Movements. The 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, 1288. http://dx.doi.org/10.1109/BioRob.2012.6290724
[15]	Corbett, E.A., Kording, K.P. and Perreault, E.J. (2013) Real-Time Evaluation of a Non-Invasive Neuroprosthetic Interface for Control of Reach. IEEE, 1-9.
[16]	Simon, A.M., Hargrove, L.J., Lock, B.A. and Kuiken, T.A. (2011) Target Achievement Control Test: Evaluating Real-Time Myoelectric Pattern-Recognition Control of Multifunctional Upper-Limb Prostheses. Journal of Rehabilitation Research & Development, 48, 619-628. http://dx.doi.org/10.1682/JRRD.2010.08.0149