Trajectory Planning and Realizing of an Exoskeleton Device for Hand Rehabilitation Based on sEMG Control

Article Preview

Abstract:

This paper presents a novel exoskeleton training system for hand rehabilitation with the surface electromyography signals (sEMG) feedback to meet the kinematics characteristics of finger joints. In this system, the sEMG is obtained by means of the surface electrodes loaded the forearm antagonistic muscle. The sEMG is amplified about 100 times again after it was preliminarily amplified about 200 times and filter,. Through threshold and the equivalent calculation, the control signal was input into the driving circuit of DC micro motor to control the hand Exoskeleton. The experimental results show that the processed sEMG can drive the hand exoskeleton for rehabilitation training, and the bending angular of joints agree well with bionics laws. The results possess guideline value for bionics motion control of hand rehabilitation with function damage.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1015-1020

Citation:

Online since:

April 2014

Export:

Price:

* - Corresponding Author

[1] M. Zecca, S. Micera, M. C. Carrozza, et al. Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal. Critical Reviews in Biomedical Engineering. 2002, 30(4-6): 459-485.

DOI: 10.1615/critrevbiomedeng.v30.i456.80

Google Scholar

[2] In, H. K. and Cho, K. J., Compact Hand Exoskeleton Robot for the Disabled, Proc. of the International Conference on Ubiquitous Robots and Ambient Intelligence, (2009).

Google Scholar

[3] Mulas, M., Folgheraiter, M. and Gini, G., An EMG controlled exoskeleton for hand rehabilitation, Proc. of the9th International Conference on Rehabilitation Robotics, pp.371-374, (2005).

DOI: 10.1109/icorr.2005.1501122

Google Scholar

[4] Tong, K. Y., Ho, S. K., Pang, P. M. K., Hu, X. L., Tam, W. K., Fung, K. L., Wei, X. J., Chen, P. N. and Chen, M., An intention driven hand functions task training robotic system, Proc. of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3406-3409, (2010).

DOI: 10.1109/iembs.2010.5627930

Google Scholar

[5] Wege, A. and Zimmermann, A., Electromyography sensor based control for a hand exoskeleton, Proc. of the IEEE International Conference on Robotics and Biomimetics, pp.1470-1475, (2007).

DOI: 10.1109/robio.2007.4522381

Google Scholar

[6] Hasegawa, Y., Tokita, J., Kamibayashi, K. and Sankai, Y., Evaluation of fingertip force accuracy in different supportconditions of exoskeleton, Proc. of the IEEE International Conference on Robotics and Automation, pp.680-685, (2011).

DOI: 10.1109/icra.2011.5980512

Google Scholar

[7] Yang Qinghua, Zhang Libing, Ruan Jian. Investigation into the Motion Law of Human Finger Joint during Grasping[J]. China Mechanical Engineering, 2004, 15(13): 1154-1157.

Google Scholar

[8] Carlo J, De Luca. The Use of Surface Electromyography in Biomechanics [J]. Appl. Biomech, 1993: 135-163.

Google Scholar

[9] Herberts P, Almstrom C. Clinical application study of multi-functional prosthetic hands [J]. Bone Joint Surgery, 1978, 60(4): 522-560.

DOI: 10.1302/0301-620x.60b4.711808

Google Scholar

[10] Wan Sha. Preliminary research of sEMG signal real-time detection and prosthetic hand control [D]. ChongqingUniversity, (2012).

Google Scholar

[11] Li Qingling. Study on sEMG based exoskeletal robot for upper limbs rehabilitation [D]. HarbinInstituteofTechnology, (2009).

Google Scholar

[12] P.A. Kaplanis C.S. Pattichis. Surface EMG analysis on normal subjects based on isometric voluntary contraction [J]. Journal of Electromyography and Kinesiology, 2009, 19: 157–171.

DOI: 10.1016/j.jelekin.2007.03.010

Google Scholar

[13] Song Lingling. Study on sEMG analysis system for prosthesis control [D]. Shanghai University, (2008).

Google Scholar

[14] Zhu Hao, Xin Changyu, Ji Xiaojun, Shi Wenkang. Surface EMG preamplifier and data acquisition system [J]. Measurement and Control Technology, 2005, 27(3): 37-39.

Google Scholar