The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

Adaptive sEMG Pattern Recognition Algorithm using Principal Component Analysis

주성분 분석을 활용한 적응형 근전도 패턴 인식 알고리즘

  • Sejin Kim (Department of Mechanical Engineering, POSTECH) ;
  • Wan Kyun Chung (Department of Mechanical Engineering, POSTECH)
  • Received : 2024.03.21
  • Accepted : 2024.05.21
  • Published : 2024.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Pattern recognition for surface electromyogram (sEMG) suffers from its nonstationary and stochastic property. Although it can be relieved by acquiring new training data, it is not only time-consuming and burdensome process but also hard to set the standard when the data acquisition should be held. Therefore, we propose an adaptive sEMG pattern recognition algorithm using principal component analysis. The proposed algorithm finds the relationship between sEMG channels and extracts the optimal principal component. Based on the relative distance, the proposed algorithm determines whether to update the existing patterns or to register the new pattern. From the experimental result, it is shown that multiple patterns are generated from the sEMG data stream and they are highly related to the motion. Furthermore, the proposed algorithm has shown higher classification accuracy than k-nearest neighbor (k-NN) and support vector machine (SVM). We expect that the proposed algorithm is utilized for adaptive and long-lasting pattern recognition.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2020R1I1A2074953).

References

  1. M. Zheng, M. S. Crouch, and M. S. Eggleston, "Surface electromyography as a natural human-machine interface: a review," IEEE Sensors Journal, vol. 22, no. 10, pp. 9198-9214, May, 2022, DOI: 10.1109/JSEN.2022.3165988. 
  2. M. R. Williams and R. F. Kirsch, "Evaluation of head orientation and neck muscle EMG signals as command inputs to a human-computer interface for individuals with high tetraplegia," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 5, pp. 485-496, Oct., 2008, DOI: 10.1109/TNSRE.2008.2006216. 
  3. K. Gui, H. Liu, and D. Zhang, "A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton," IEEE/ASME Transactions on Mechatronics, vol. 24, no. 2, pp. 483-494, Apr., 2019, DOI: 10.1109/TMECH.2019.2893055. 
  4. M. Ghassemi, K. Triandafilou, A. Barry, M. E. Stoykov, E. Roth, F. A. Mussa-Ivaldi, D. G. Kamper, and R. Ranganathan, "Development of an EMG-controlled serious game for rehabilitation," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 2, pp. 283-292, Feb., 2019, DOI: 10.1109/TNSRE.2019.2894102. 
  5. C. Yang, J. Luo, C. Liu, M. Li, and S.-L. Dai, "Haptics electromyography perception and learning enhanced intelligence for teleoperated robot," IEEE Transactions on Automation Science and Engineering, vol. 16, no. 4, pp. 1512-1521, Oct., 2018, DOI: 10.1109/TASE.2018.2874454. 
  6. C. Shen, Z. Pei, W. Chen, J. Wang, J. Zhang, and Z. Chen, "Toward generalization of sEMG-based pattern recognition: A novel feature extraction for gesture recognition," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, Jan., 2022, DOI: 10.1109/TIM.2022.3141163. 
  7. Y. Zheng, G. Xu, Y. Li, and W. Qiang, "Improved online decomposition of non-stationary electromyogram via signal enhancement using a neuron resonance model: A simulation study," Journal of Neural Engineering, vol. 19, no. 2, Apr., 2022, DOI: 10.1088/1741-2552/ac5f1b. 
  8. A. Furui, T. Igaue, and T. Tsuji, "EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models," Expert Systems with Applications, vol. 185, Dec., 2021, DOI: 10.1016/j.eswa.2021.115644. 
  9. H. Wang, P. Huang, T. Xu, G. Li, and Y. Hu, "Towards zero retraining for multiday motion recognition via a fully unsupervised adaptive approach and fabric myoelectric armband," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 217-225, Jan., 2022, DOI: 10.1109/TNSRE.2022.3144323. 
  10. N. Zheng, Y. Li, W. Zhang, and M. Du, "User-independent emg gesture recognition method based on adaptive learning," Frontiers in Neuroscience, vol. 16, pp. 1-13, Mar., 2022, DOI: 10.3389/fnins.2022.847180. 
  11. R. Soroushmojdehi, S. Javadzadeh, A. Pedrocchi, and M. Gandolla, "Transfer learning in hand movement intention detection based on surface electromyography signals," Frontiers in Neuroscience, vol. 16, pp. 1-18, Nov., 2022, DOI: 10.3389/fnins.2022.977328. 
  12. S. Park, W. K. Chung, and K. Kim, "Training-free Bayesian self-adaptive classification for sEMG pattern recognition including motion transition," IEEE Transactions on Biomedical Engineering, vol. 67, no. 6, pp. 1775-1786, Jun., 2020, DOI: 10.1109/TBME.2019.2947089. 
  13. S. Park, H.-J. Lee, W. K. Chung, and K. Kim, "Training-Free sEMG Pattern Recognition Algorithm: A Case Study of A Patient with Partial-Hand Amputation," Journal of Korea Robotics Society, vol. 14, no. 3, pp. 211-220, Aug., 2019, DOI: 10.7746/jkros.2019.14.3.211. 
  14. T. Matsubara and J. Morimoto, "Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface," IEEE Transactions on Biomedical Engineering, vol. 60, no. 8, pp. 2205-2213, Aug., 2013, DOI: 10.1109/TBME.2013.2250502. 
  15. Z. Li, B. Wang, C. Yang, Q. Xie, and C.-Y. Su, "Boosting-based EMG patterns classification scheme for robustness enhancement," IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 3, pp. 545-552, May, 2013, DOI: 10.1109/JBHI.2013.2256920. 
  16. A. H. Al-Timemy, G. Bugmann, J. Escudero, and N. Outram, "Classification of finger movements for the dexterous hand prosthesis control with surface electromyography," IEEE journal of biomedical and health informatics, vol. 17, no. 3, pp. 608-618, May, 2013, DOI: 10.1109/JBHI.2013.2249590. 
  17. A. Phinyomark, P. Phukpattaranont, and C. Limsakul, "Feature reduction and selection for EMG Signal Classification," Expert Systems with Applications, vol. 39, no. 8, pp. 7420-7431, Jun., 2012, DOI: 10.1016/j.eswa.2012.01.102. 
  18. C. J. De Luca, L. Donald Gilmore, M. Kuznetsov, and S. H. Roy, "Filtering the surface EMG Signal: Movement artifact and Baseline Noise Contamination," Journal of Biomechanics, vol. 43, no. 8, pp. 1573-1579, May, 2010, DOI: 10.1016/j.jbiomech.2010.01.027. 
  19. M. A. Oskoei and H. Hu, "Myoelectric Control Systems-a survey," Biomedical Signal Processing and Control, vol. 2, no. 4, pp. 275-294, Oct., 2007, DOI: 10.1016/j.bspc.2007.07.009. 
  20. S. M. S. Shah, S. Batool, I. Khan, M. U. Ashraf, S. H. Abbas, and S. A. Hussain, "Feature extraction through parallel probabilistic principal component analysis for heart disease diagnosis," Physica A: Statistical Mechanics and its Applications, vol. 482, pp. 796-807, Sept., 2017, DOI: 10.1016/j.physa.2017.04.113. 
  21. K. K. Vasan and B. Surendiran, "Dimensionality reduction using principal component analysis for network intrusion detection," Perspectives in Science, vol. 8, pp. 510-512, Sept., 2016, DOI: 10.1016/j.pisc.2016.05.010. 
  22. A. C. Turlapaty and B. Gokaraju, "Feature analysis for classification of physical actions using surface EMG data," IEEE Sensors Journal, vol. 19, no. 24, pp. 12196-12204, Dec., 2019, DOI: 10.1109/JSEN.2019.2937979. 
  23. F. Amirabdollahian and M. L. Walters, "Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband," 2017 IEEE International Conference on Rehabilitation Robotics(ICORR), London, UK, pp. 111-115, 2017, DOI: 10.1109/ICORR.2017.8009231. 
  24. X. Zhai, B. Jelfs, R. H. M. Chan, and C. Tin, "Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network," Frontiers in Neuroscience, vol. 11, pp. 1-11, Jul., 2017, DOI: 10.3389/fnins.2017.00379. 
  25. A. Subasi, "Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders," Computers in Biology and Medicine, vol. 43, no. 5, pp. 576-586, Jun., 2013, DOI: 10.1016/j.compbiomed.2013.01.020.