Training-Free sEMG Pattern Recognition Algorithm: A Case Study of A Patient with Partial-Hand Amputation

무학습 근전도 패턴 인식 알고리즘: 부분 수부 절단 환자 사례 연구

  • Received : 2019.05.28
  • Accepted : 2019.06.13
  • Published : 2019.08.30


Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.


Supported by : National Research Foundation of Korea


  1. 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.
  2. M. Hakonen, H. Piitulainen, and A. Visala, "Current state of digital signal processing in myoelectric interfaces and related applications," Biomedical Signal Processing and Control, vol. 18, pp. 334-359, Apr., 2015.
  3. Y.-J. Kim, D.-H. Lee, H. Park, J.-H. Park, and J.-H. Bae, "A Novel Input Device for Robotic Prosthetic Hand: Design and Preliminary Results," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 2018.
  4. C. Potluri, M. Anugolu, D. S. Naidu, M. P. Schoen, and S. C. Chiu, "Real-time embedded frame work for sEMG skeletal muscle force estimation and LQG control algorithms for smart upper extremity prostheses," Engineering Applications of Artificial Intelligence, vol. 46, pp. 67-81, Nov., 2015.
  5. J. He, D. Zhang, N. Jiang, X. Sheng, D. Farina, and X. Zhu, "User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control," Journal of Neural Engineering, vol. 12, Jun., 2015.
  6. H.-J. Hwang, J. M. Hahne, and K.-R. Muller, "Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom," Journal of Neural Engineering, vol. 11, no. 5, Aug., 2014.
  7. F. Clemente, M. D'Alonzo, M. Controzzi, B. B. Edin, and C. Cipriani, "Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 12, pp. 1314-1322, Dec., 2016.
  8. G. R. Naik, A. H. Al-Timemy, and H. T. Nguyen, "Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 8, pp. 837-846, Aug., 2016.
  9. Y.-J. Kim, D.-H. Lee, H. Park, J.-H. Park, J.-H. Bae, and M.-H. Baeg, "Wrist and Grasping Forces Estimation using Electromyography for Robotic Prosthesis," Journal of Korea Robotics Society, vol. 12, no. 2, pp. 206-216, Jun., 2017.
  10. Q. Cheng, H. Zhou, and J. Cheng, "The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1217-1233, Jun., 2011.
  11. Y. Su, S. Shan, X. Chen, and W. Gao, "Classifiability-Based Discriminatory Projection Pursuit," IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2050-2061, Dec., 2011.
  12. S. Soman and Jayadeva, "High performance EEG signal classification using classifiability and the Twin SVM," Applied Soft Computing, vol. 30, pp. 305-318, May, 2015.
  13. Y. Kamei and S. Okada, "Classification of forearm and finger motions using electromyogram and arm-shape-changes," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 2016, DOI: 10.1109/EMBC.2016.7592016.
  14. F. Riillo, L.R. Quitadamo, F. Cavrini, E. Gruppioni, C.A. Pinto, N. Cosimo Pasto, L. Sbernini, L. Albero, and G. Saggio, "Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees," Biomedical Signal Processing and Control, vol. 14, pp. 117-125, Nov., 2014.
  15. A. Xiong, X. Zhao, J. Han, G. Liu, and Q. Ding, "An user-independent gesture recognition method based on sEMG decomposition," 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, DOI: 10.1109/IROS.2015.7353969.
  16. D. Ge, E. Le Carpentier, and D. Farina, "Unsupervised Bayesian Decomposition of Multiunit EMG Recordings Using Tabu Search," IEEE Transactions on Biomedical Engineering, vol. 57, no. 3, pp. 561-571, Mar., 2010.
  17. M. Karg, G. Venture, J. Hoey, and D. Kulic, "Human Movement Analysis as a Measure for Fatigue: A Hidden Markov-Based Approach," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 3, pp. 470-481, May, 2014.
  18. Q. Zhang, M. Hayashibe, P. Fraisse, and D. Guiraud, "FES-Induced Torque Prediction With Evoked EMG Sensing for Muscle Fatigue Tracking," IEEE/ASME Transactions on Mechatronics, vol. 16, no. 5, pp. 816-826, Oct., 2011.
  19. G.-C. Jeong, Y. Kim, W. Choi, G. Gu, H.-J. Lee, M. B. Hong, and K. Kim, "On the Design of a Novel Underactuated Robotic Finger Prosthesis for Partial Hand Amputation," IEEE RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), Toronto, Canada, 2019.
  20. C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, Springer New York, 2006.
  21. A. D. Bellingegni, E. Gruppioni, G. Colazzo, A. Davalli, R. Sacchetti, E. Guglielmelli, and L. Zollo, "NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation," Journal of NeuroEngineering and Rehabilitation, vol. 14, no. 1, p. 82, Aug., 2017.
  22. K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015, DOI: 10.1109/ICCV.2015.123.