Cover
Vol. 6 No. 2 (2010)

Published: November 30, 2010

Pages: 158-166

Original Article

A k-Nearest Neighbor Based Algorithm for Human Arm Movements Recognition Using EMG Signals

Mohammed Z. Al-Faiz, MIEEE 1 Abduladhem A.Ali 2 Abbas H. Miry 3
1Computer Engineering Dept., Nahrain University, Baghdad, Iraq
2Computer Engineering Dept, Basrah University, Basrah, Iraq
3Electrical Engineering Dept., AL-Mustansiriyah University, Baghdad, Iraq
History
  • Received: September 30, 2010
  • Available Online: November 30, 2010
DOI

https://doi.org/10.37917/ijeee.6.2.12

Abstract

In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor (K-NN) for different movements of a prosthetic arm. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with the conventional recognition method using Artificial Neural Network (ANN). Results show the proposed K-NN technique achieved a uniformly good performance with respect to ANN in terms of time, which is important in recognition systems, and better accuracy in recognition when applied to lower Signal-to-Noise Ratio (SNR) signals.

Keywords

References

  1. O. Fukuda, T. Tsuji, M. Kaneko, and A. Otsuka, "A human-assisting manipulator teleported by emg signals and arm motions," IEEE Trans. on Robotics and Automation, Vol. 19, No. 2, pp. 210-222.2000.
  2. P. K. Artemiadis and K. J. Kyriakopoulos." EMG-based Teleoperation of a Robot Arm Using Low-Dimensional Representation", Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, Oct. 2007, pp. 489 495.
  3. N.Bu, M. Okamoto, and T, Tsuji, "A Hybrid Motion Classification Approach for EMG-Based Human-Robot Interfaces Using Bayesian and Neural Networks "IEEE Trans. on Robot. Vol. 23, No. 3, pp. 502-511, June 2009.
  4. S.Park and S.Lee," EMG Pattern Recognition Based on Artificial Intelligence Techniques", IEEE Trans on Rehabilitation Engineering, Vol. 6, No. 4, Dec 1998 ,pp:400-405.
  5. H.Huang Y.Liu and C.Wong,"Automatic EMG Feature Evaluation for Controlling a Prosthetic Hand Using a Supervised. Feature Mining Method: An Intelligent Approach "Proceeding of the IEEE, International Conference on Robotics & Automation,Taiwan September 14-19,2003 pp:220-225
  6. M. Z. Al-Faiz, Y. I. Al-Mashhadany, "Human Arm Movements Recognition Based on EMG Signal "MASAUM Journal Of Basic and Applied Sciences (MJBAS) Volume 1 Issue 2, PP 164-171, (September 2009)
  7. O. Bida, Influence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimation", M.Sc Thesis, WORCESTER POLYTECHNIC INSTITUTE, Electrical Engineering, January 2005
  8. K. Kiguchi, S. Watanabe, K. Izumi and T.Fukuda," An Exoskeletal Robot for Human Elbow Motion Support Sensor Fusion, Adaptation, and Control", IEEE Trans on System, Man and Cybernetics, Vol. 31, No. 3, June 2001 pp:353-361.
  9. R. Boostani and M. Moradi," Evaluation of the forearm EMG signal features for the control of a prosthetic hand", Institute of Physics Publishing Physiological Measurement Physiol. Meas. 24 2003 pp: 309-319.
  10. A. Phinyomark, C. Limsakul, and P. Phukpattaranont," A Novel Feature Extraction for Robust EMG Pattern Recognition", Journal of Computing, ISSN: 2151-9617, Vol.1, No. 1, Dec 2009, pp:71-80.
  11. R. Elliott "Feature Extraction Techniques for Grasp Classification ",Master Thesis, University of Canterbury, August 1998.
  12. L. Wang and T. S. Buchanan, "Prediction of joint moments using a neural network model of muscle activations from emg signals," IEEE Transactions on Neural System and Rehabilation Engineering, vol. 10, pp. 30-37, 2002.
  13. T. M. Cover and P. E. Hart, "Nearest neighbor pattern classification, "IEEE Trans. Inform. Theory, vol. IT-13, pp. 21-26, 1967.
  14. H. Fayed and A. Atiya, "A Novel Template Reduction Approach for the Nearest Neighbor Method", IEEE Trans on Neural Network, Vol. 20, No. 5, May 2009.pp:890-896.
  15. A. Cervantes, 1. Galván, and P. Isasi", AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification", IEEE Trans on System, Man and Cybernetics, Vol. 39, No. 5, Oct 2009 pp:1082-1091.
  16. N. Pal and S. Ghosh, "Some Classification Algorithms Integrating Dempster Shafer Theory of Evidence with the Rank Nearest Neighbor Rules", IEEE Trans on Systems, Man,and Cybernetics-Part A, Vol. 31, No. 1, pp: 59-66 Jan 2001.