The Robot Hand System that can Control Grasping Power by SEMG
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.
[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
[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
@article{"International Journal of Medical, Medicine and Health Sciences:51177", author = "Tsubasa Seto and Kentaro Nagata and Kazushige Magatani", title = "The Robot Hand System that can Control Grasping Power by SEMG", abstract = "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 = "SEMG, multi electrode, robot hand, power control", volume = "6", number = "9", pages = "420-4", }