A Study on Design of FES Hardware System for Walking of Paraplegics

하반신마비 환자의 보행기능 제어를 위한 FES하드웨어 시스템 설계에 관한 연구

  • 김근섭 (서울시립대학교 전자공학과) ;
  • 김종원 (서울시립대학교 전자공학과)
  • Published : 1991.03.01

Abstract

This paper describes and discusses the employment of HMG pattern analysis to provide upper-motor-neuron paraplegics with patient-responsive control of FES ( functional electrical stimulation) for the purpose of walker-supported walking. The use of above-lesion EMG signals as a solution to the control problem is considered. The AR(autoregressive)parameters are identified by time-varying nonstationary Kalman filler algorithm using DSP chip and classified by fuzzy theory. The control and stimuli part of the below-lesion are based on micro-processor(8031). The designed stimulator is a 4-channel version. The experiments described above have only attempted to discriminate between standing function and sit-down function A further advantge of the this system Is applied for motor rehabilitation of social readaption of paralyzed humans.

Keywords

References

  1. IEEE Trans on BME v.27 no.7 Myoelectric signal processing: Optimal estimation applied to electromyography-Part I : derivation of the optimal myoprocessor Neville Hogan(et al.)
  2. IEEE Trans on BME v.27 no.7 Myoelectric signal processing: Optimal estimation applied to electromyography-Part Ⅱ : experimental demonstration of optimal myoprocessor performance Neville Hogan(et al.)
  3. IEEE Trans on BME v.29 no.6 EMG pattern analysis and classification for a prosthetic arm George N. Saridis(et al.)
  4. IEEE Trans on BME v.31 no.Feb Signal processing for propotional myoelectric control Harry B. Evans(et al.)
  5. IEEE Trans on BME v.31 no.3 An Algorithm for Sequential Signal Estimation and System Identification for EMG Signals Rui J. P de Figueiredo;George S. Moschytz(et al.)
  6. IEEE Trans on Automatic Control v.29 no.4 The control of a prosthetic arm by EMG pattern recognition Lee,S.H.;Saridis,G.N.
  7. IEEE Trans on BME v.31 no.12 A New Framework and Compuer Program for Quantitative EMG Signal Analysis Rui J. P de Figueiredo;George S. Moschytz(et al.)
  8. 인하대학교 대학원 석하학위 논문 신경회로망과 확률 모델을 이용한 근전도의 패턴 분류에 관한 연구 장영건
  9. Annual International Conference of the IEEE engineering in MBS v.12 no.1 EMG amplitude estimation from temporally whitened, spacially uncorrelated multiple channel EMG Edward A. Clancy;Neville, Hogan
  10. Proc. IEEE v.65 Signal processing for the multistate myoelectric channel P.A.Parker;J.A.Stuller(et al.)
  11. IEEE Trans on BME v.34 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;Walter, Zucchini;George G. Jaros
  12. IEEE Trans on BME v.35 no.4 The Electromyogram(EMG) as a control signal for Functional neuromuscular Stimulation-Part Ⅱ Practical Demonstaration of the EMG signature Discrimination System Gisela, Hefftner;George G. Jaros
  13. IEEE Trans on BME v.36 no.10 The Experimental Demonstration of a Multichannel Time-series Myoprocessor : System Testing and Evaluation Ronald J.Triolo;Gorden D. Moskwitz
  14. New Developments in EMG and Clinical Neurophysiology Surface electromyograohy in relation to force, muscle length and endurance J.Vredenbregt(et al.);J.E.Desmedt(Ed.)
  15. Applied Statistical Decision Theory H.Raiffa;R.Schaiffer