Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31200
The Robot Hand System that can Control Grasping Power by SEMG

Authors: Tsubasa Seto, Kentaro Nagata, Kazushige Magatani


SEMG (Surface Electromyogram) is one of the bio-signals and is generated from the muscle. And there are many research results that use forearm EMG to detect hand motions. In this paper, we will talk about our developed the robot hand system that can control grasping power by SEMG. In our system, we suppose that muscle power is proportional to the amplitude of SEMG. The power is estimated and the grip power of a robot hand is able to be controlled using estimated muscle power in our system. In addition, to perform a more precise control can be considered to build a closed loop feedback system as an object to a subject to pressure from the edge of hand. Our objectives of this study are the development of a method that makes perfect detection of the hand grip force possible using SEMG patterns, and applying this method to the man-machine interface.

Keywords: Power Control, robot hand, sEMG, multi electrode

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1649


[1] B. Hudgins, P. Parker, and R. N. Scott, "A newstrategy for multifunction myoelectric control," IEEE Trans. Biomed. Eng., vol. 40, pp.82.94, Jan. 1993.
[2] H.-P. Huang, and C.-Y. Chen "Development of a Myoelectric Discrimination System for a MultiDegree Prosthetic Hand," in Proc. 1999, Int. Conf. IEEE on Robotics & Automation , Detroit, Michigan May 1999
[3] Y. Al-Assaf and H. Al-Nashash, "Surface myoelectric signal classification for prostheses control," J. Med. Eng. Technol., vol. 29, pp. 203.207, Sep./Oct. 2005
[4] Y. Huang, K. B. Englehart, B. Hudgins, and A. D. Chan, "A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses," IEEE Trans. Biomed. Eng., vol. 52, no. 11, pp. 1801.1811, Nov. 2005.
[5] F. H. Y. Chan, Y. S. Yang, F. K. Lam, Y. T. Zhang, and P. A. Parker, "Fuzzy EMG classification for prosthesis control," IEEE Trans. Rehab. Eng., vol. 8, pp. 305.311, Sep. 2000.
[6] K. Ando, K. Magatani et al.:"Development of the input equipment for a computer using surface EMG" Proceedings of the 28th IEEE EMBS Annual International Conference (2006)
[7] K. Nagata, M. Yamada, and K. Magatani, "Development of the assist system to operate a computer for the disabled using multichannel surface EMG," in Proc. 26th Ann. Int. IEEE Conf. Eng. Med. Biol., San Francisco, 2004, pp.4952-4955