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Gait Angle Prediction for Lower Limb Orthotics and Prostheses Using an EMG Signal and Neural Networks

Lee Ju-Won    (Department of Electronic Engineering and ERI, Gyeongsang National University   ); Lee Gun-Ki    (Department of Electronic Engineering and ERI, Gyeongsang National University  );
  • 초록

    Commercial lower limb prostheses or orthotics help patients achieve a normal life. However, patients who use such aids need prolonged training to achieve a normal gait, and their fatigability increases. To improve patient comfort, this study proposed a method of predicting gait angle using neural networks and EMG signals. Experimental results using our method show that the absolute average error of the estimated gait angles is $0.25^{\circ}$ . This performance data used reference input from a controller for the lower limb orthotic or prosthesis controllers while the patients were walking.


  • 주제어

    EMG .   prosthesis .   gait angle predictor .   human computer interaction .   neural networks .   orthotic.  

  • 참고문헌 (8)

    1. L. Wang and T. S. Buchanan, 'Prediction of joint moments using a neural network mode of muscle activations from EMG signals,' IEEE Trans. on Rehabilitation Engineering, vol. 10, no. 1, pp. 30-37, 2002 
    2. N. Ozkaya and M. Nordin, Fundamental Biomechanics; Equilibrium, Motion, and Deformation, Springer, New York, USA, 1998 
    3. L. Lee, Neural Fuzzy System, Prentice Hall, 1996 
    4. A. Barreto, S. Scargle, and M. Adjouadi, 'Realtime digital EMG/EEG signal processing in a human-computer interface for users with severe motor disabilities,' Proc. of the International Conference on Signal Processing Applications & Technology, Orlando, Florida, November 1-4, 1999 
    5. A. Barreto, S. Scargle, and M. Adjouadi, 'A practical EMG-based human-computer interface for users with motor disabilities,' Journal of Rehabilitation Research & Development, vol. 37, no. 1, pp. 53-63, 2000 
    6. Y. Koike and M. Kawato, 'Trajectory formation from surface EMG signals using a neural network model,' Japan EIC, D-II, vol. J77-D-II, no.1, pp.193-203, 1994 
    7. J. M. Zurada, Introduction to Artificial Neural Systems, West Publishing Company, pp. 206-218, 1992 
    8. F. H. Chan, Y. S. Yang, F. K. Lam, Y. T. Zhang, and P. A. Parker, 'Fuzzy EMG classification for prosthesis control,' IEEE Trans. on Rehabilitation Engineering, vol. 8, no. 3, pp. 305-311, 2000 

 저자의 다른 논문

  • Lee Ju-Won (2)

    1. 2005 "Adaptive Postural Control for Trans-Femoral Prostheses Based on Neural Networks and EMG Signals" International journal of precision engineering and manufacturing 6 (3): 37~44    
    2. 2005 "Design of an Adaptive Filter with a Dynamic Structure for ECG Signal Processing" International Journal of Control, Automation and Systems 3 (1): 137~142    
  • Lee Gun-Ki (2)

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