Browse > Article

A study on bio-signal process for prosthesis arm control  

Ahn, Young-Myung (Dept. of Automobile Engineering, Seoil College)
Yoo, Jae-Myung (Educational Training Center for the Next Generation Intelligent Robot)
Publication Information
전자공학회논문지 IE / v.43, no.4, 2006 , pp. 28-36 More about this Journal
Abstract
In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.
Keywords
Rehabilitaion Engineering; EMG(electromyogram); prosthesis; flex sensor; fuzzy controller;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Xiong, F.Q., Shwedyk.Ed., 'Some aspects of nonstationary myoelectric signal processing,' IEEE Trans. Biomedical Engineering, Vol. BME - 34, pp. 166-172, 1987   DOI
2 Wirta, R.W., Taylor, D.R., Finley, F.R., 'Pattern recognition arm prostheses : A historical perspective-A final report,' Bull Prothes. Research, pp. 8-35, 1978
3 Atal, B.S., 'Effectiveness of linear prcxliction characteristics of the speech wave for automatic sperker identification and verification,' JASA., Vol. 55, pp. 1304-1312, 1974   DOI
4 Stulen, F.B., Luca, C.J., 'Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity,' IEEE Tran. Biomedical Engineering, Vol. BME - 28, pp. 515-523, 1981   DOI
5 Park, S., Lee, S., 'EMG Pattern Recognition based on Artificial Intelligence Techniques,' IEEE. Trans. on Rehabilitation Engineering, Vol. 6, No.4, 1998
6 O'Neill. P.A., 'Myoelectric singal characteristics from muscles in residual upper limbs,' IEEE Trans. Rehabilitation Engineering, Vol. 2, No.4, 1994
7 Troilo, R.J., Moskowizt, G.D., 'Theoretical development of a multichannel time-series myoprocessor for simultaneous limb function detection and muscle force estimation,' IEEE Trans, Biomedical Engineering, Vol. BME - 36, No. 10, pp. 1004-1017, 1989
8 Hannaford, B., Lehman, S., 'Short time fourier analysis of the electromyogram : Fast movements and constant constraction,' IEEE Trans. Biomedical Engineering, Vol BME - 33, No. 12, 1987
9 2004년 보건복지백서, 보건복지부
10 Kelly, M.F., Parker, P.A, Scott, R.N., 'The application of neural networks to myoelectric signal analysis: A preliminary study,' IEEE Trans Biomedical Engineering, Vol. 37, No.3, pp. 221-230, 1990   DOI   ScienceOn
11 Markhoul, J.D., 'Linear Prediction: A tutorial review,' Proceedings IEEE, Vol. 63, pp. 561-579, 1975
12 Kiryu, T., Saitoh, S., Ishioka, K., 'Inverstigation on parametric analysis of dynamic EMG signals by a musclc-sutrutured simulation model,' IEEE Trans. Biomedical Engineering, Vol. BME - 39, pp. 280-288, 1992
13 Ozyilmaz, L., Yilclirim, T., Seker, H., 'EMG Signal Classification Using Conic Section Function Neural Networks,' International Joint Conference on Neural networks, Vol. 5, pp. 3601-3603, 1999
14 Kreifeldt, J. 'Signal versus noise characteristics of filtered EMG used as a control source,' IEEE Trans. Biomedical Engineering, Vol. BME -18, pp. 16-22, 1971   DOI
15 Studer, R.M., 'An algorithm for sequential signal estimation and system identification for EMG signals,' IEEE Trans. Biomedical Engineering, Vol. BME-31, No.3, pp. 285-295, 1984   DOI   ScienceOn
16 Sheriff, M.H., 'Effects of load on myoelectric signals : the ARMA representation,' IEEE Trans., Vol. BME - 28, pp. 411-416, 1981
17 AI-Assaf, Al-Nashash, Y., 'Myoelectric signal segmentation and classification using wavelets based neural networks,' 2001. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 2, pp. 1820 -1823, 2001