Adaptive Postural Control for Trans-Femoral Prostheses Based on Neural Networks and EMG Signals

  • Lee Ju-Won (Depart. of Electronic Engineering, Gyeongsang National University) ;
  • Lee Gun-Ki (Depart. of Electronic Engineering, Gyeongsang National University)
  • 발행 : 2005.07.01

초록

Gait control capacity for most trans-femoral prostheses is significantly different from that of a normal person, and training is required for a long period of time in order for a patient to walk properly. People become easily tired when wearing a prosthesis or orthosis for a long period typically because the gait angle cannot be smoothly adjusted during wearing. Therefore, to improve the gait control problems of a trans-femoral prosthesis, the proper gait angle is estimated through surface EMG(electromyogram) signals on a normal leg, then the gait posture which the trans-femoral prosthesis should take is calculated in the neural network, which learns the gait kinetics on the basis of the normal leg's gait angle. Based on this predicted angle, a postural control method is proposed and tested adaptively following the patient's gait habit based on the predicted angle. In this study, the gait angle prediction showed accuracy of over $97\%$, and the posture control capacity of over $90\%$.

키워드

참고문헌

  1. Koike, Y. and Kawato, M., 'Trajectory Formation from Surface EMG Signals Using a Neural Network Model,' EIC, D-II, Vol. J77-D-II, No. 1, 1994
  2. Barreto, A., Scargle, S. and Adjouadi,M. , 'A Practical EMG-based Human-Computer Interface for Users with Motor Disabilities,' Journal of Rehabilitation Research & Development, Vol 37, No. 1, 2000
  3. Barreto, A., Scargle, S. and Adjouadi, M. , 'Real-Time Digital EMG/EEG Signal Processing in a Human-Computer Interface for Users with Severe Motor Disabilities,' Proceedings of the International Conference on Signal Processing Applications & Technology (ICSPAT), 1999
  4. Esquenanzi, A., Keenan, M., 'Gait Analysis In Rehabilition Medicine,' Principles and Practice, Delisa JA, Gans BM (Eds.), JB Lippincott Co., 1993
  5. Ozkaya, N., Nordin, M., 'Fundamentals of Biomechanics,' Springer-Verlag, 1999
  6. Wang, L., Bunchanan, T.S., 'Prediction of Joint Moments Using a Neural NetworkMode of Muscle Activations From EMG Signals,' IEEE, Trans. on Rehabilitation Engineering, Vol 10, No. 1, pp.30-37, 2002 https://doi.org/10.1109/TNSRE.2002.1021584
  7. Enderle, J., 'Introduction to Biomedical Engineering,' Academic Press, 2000
  8. Akay,M., 'Biomedical Signal Processing,' Academic Press, 1994
  9. Kalanovic, V.D., Popovic, D., Skaug, N.T. 'Feedback Error Learing Neural Network for Trans-Femoral Prosthesis,' IEEE, Trans. on Rehabilitation Engineering, Vol. 8, No. 1, pp. 71-80, 2000 https://doi.org/10.1109/86.830951
  10. Lee, J.W., Lee, G.K., 'Noise Filtering of ECG Signal using RBF Neural Networks,' Journal of the Korean Institute of Maritime Information and Communication Sciences, Vol. 3, No. 3, pp. 553-558, 1999
  11. Mckerrow, P. J., 'Introduction to Robotics,' Addison-Wesley, 1993
  12. Zurada, J.M, 'Introduction to Artificial Neural System,' West Publishing Company, 1992
  13. Lee, L., 'Neural Fuzzy System,' Printice Hall, 1996
  14. DAS, 'Tilt SA1 Sensor Data Sheet,' Digital Advanced SensorTechnology Co., 2001
  15. Omatu, S., 'Neuro-Control and Its Applications, Springer,' 1996
  16. Ogata, K., 'Discrete-Time Control System,' Prentice-Hall Press, 1994