A Study on Human Training System for Prosthetic Arm Control

의수제어를 위한 인체학습시스템에 관한 연구

  • Published : 1994.12.01

Abstract

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

Keywords

References

  1. RESNA '94 Conference EMG Pattern Analysis for Prosthesis Arm Control Y.G.Jang;S.H.Hong(et al.)
  2. Proceedings of the 15th Annual Int. Conference of the IEEE EMBS Probabilistic Neural Pattern Classifier and Muscle Force Estimation Y.G.Jang;S.H.Hong(et al.)
  3. Proceedings JTC-CSCC '94 A Study on the Fuzzy Controller for Prosthesis Arm Control using EMG Signals Y.G.Jang;S.H.Hong
  4. IEEE Trans on BME v.29 no.6 EMG pattern analysis and classification for a Prosthetic arm G.N.Saridis(et al.)
  5. IEEE Trans on BME v.41 no.2 Single Site Electromyograph Amplitude Estimation Edward A. Clancy;Neville Horgan
  6. Preceedings of the 37th Meeting of the Society of Exploration Geophysics 1967, Reprinted in Modern Spectral Analysis Maximum Entropy Spectral Analysis Burg,J.P.;D.G.Childers(eds)
  7. Ann. Math. Statist. On Consistent Estimates of the Spectrum of a Stationary Time Series Parzen,E.
  8. IEEE Trans on BME v.26 no.6 Physiology and Mathmatics of Myoelectric Signals Carlo J. De Luca
  9. IEEE Trans on BME v.7 no.4 An application of signal processing techniques to the study of myoelectric signals Harry. G. Kwantny(et al.)
  10. IEEE Trans on SMC v.5 no.2 Functional Seperation of EMG signal via ARMA identification methods for prosthesis control purposes D.Graupe(et al.)
  11. 대한전자 공학회 논문지 v.27 no.2 LPC 켑스트럼 계수를 이용한 EMG신호의 기능인식에 관한 연구 박상희(등)
  12. IEEE Trans on BME v.35 no.4 The Electromyogram(EMG) as a control signal for Functional Neuromuscular Stimulation-Part Ⅰ: Autoregressive Modelling as a Means of EMG Signature Discrimination Gisela Hefftner(et al.)