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Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

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Abstract

This paper presents an anthropomorphic prosthetic hand using flexure hinges, which is controlled by the surface electromyography (sEMG) signals from 2 electrodes only. The prosthetic hand has compact structure with 5 fingers and 4 Degree of Freedoms (DoFs) driven by 4 independent actuators. Helical springs are used as elastic joints and the joints of each finger are coupled by tendons. The myoelectric control system which can classify 8 prehensile hand gestures is built. Pattern recognition is employed where Mean Absolute Value (MAV), Variance (VAR), the fourth-order Autoregressive (AR) coefficient and Sample Entropy (SE) are chosen as the optimal feature set and Linear Discriminant Analysis (LDA) is utilized to reduce the dimension. A decision of hand gestures is generated by LDA classifier after the current projected feature set and the previous one are “pre-smoothed”, and then the final decision is obtained when the current decision and previous decisions are “post-smoothed” from the decisions flow. The prosthetic hand can perform prehensile postures for activities of daily living and carry objects under the control of EMG signals.

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References

  1. Asghari O M, Hu H. Myoelectric control systems: A survey. Biomedical Signal Processing and Control, 2007, 2, 275–294.

    Article  Google Scholar 

  2. Chan C C, Liao W H. Temporal gait parameters captured by surface electromyography measurement. Proceedings of 2012 IEEE International Conference on Robotics and Biomimetics, Guangzhou, China, 2012, 1056–1061.

    Chapter  Google Scholar 

  3. Cannan J, Hu H S. Human-Machine Interaction (HMI): A Survey, Technical Report CES-508, University of Essex, Colchester, UK, 2011.

    Google Scholar 

  4. Chen W R, **ong C H. On adaptive grasp with underactuated anthropomorphic hands. Journal of Bionic Engineering, 2016, 13, 59–72.

    Article  Google Scholar 

  5. Connolly C. Prosthetic hands from touch bionics. Industrial Robot: An International Journal, 2008, 35, 290–293.

    Article  Google Scholar 

  6. Dalley S, Wiste T, Withrow T, Goldfarb M. Design of a multifunctional anthropomorphic prosthetic hand with extrinsic actuation. IEEE/ASME Transactions on Mechatronics, 2009, 14, 699–706.

    Article  Google Scholar 

  7. Cipriani C, Controzzi M, Carrozza M. The smarthand transradial prosthesis. Journal of Neuroengineering and Rehabilitation, 2011, 8, 29–42.

    Article  Google Scholar 

  8. Zecca M, Micera S, Carrozza M C, Dario P. Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering, 2002, 30, 459–485.

    Article  Google Scholar 

  9. Lotti F, Tiezzi P, Vassura G, Biagiotti L, Palli G, Melchiorri C. Development of UB hand 3: Early results. Proceedings of IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, 4488–4493.

    Google Scholar 

  10. Huang H, Jiang L, Liu Y, Hou L, Cai H, Liu H. The mechanical design and experiments of hit/dlr prosthetic hand. Proceedings of IEEE International Conference on Robotics and Biomimetics, Kunming, China, 2006, 896–901.

    Google Scholar 

  11. Zollo L, Roccella S, Guglielmelli E, Carrozza M, Dario P. Biomechatronic design and control of an anthropomorphic artificial hand for prosthetic and robotic applications. IEEE/ASME Transactions on Mechatronics, 2007, 12, 418–429.

    Article  Google Scholar 

  12. Gaiser I, Pylatiuk C, Schulz S, Kargov A, Oberle R, Werner T. The fluidhand iii: A multifunctional prosthetic hand. Journal of Prosthetics and Orthotics, 2009, 21, 91–96.

    Article  Google Scholar 

  13. Xu Z J, Tian Y T, Li Y. sEMG pattern recognition of muscle force of upper arm for intelligent bionic limb control. Journal of Bionic Engineering, 2015, 12, 316–323.

    Article  Google Scholar 

  14. Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. Journal of Rehabilitation Research and Development, 2011, 48, 643–659.

    Article  Google Scholar 

  15. Kent B A, Lavery J, Engeberg E D. Anthropomorphic control of a dexterous artificial hand via task dependent temporally synchronized synergies. Journal of Bionic Engineering, 2014, 11, 236–248.

    Article  Google Scholar 

  16. Lorrain T, Jiang N, Farina D. Influence of the training set on the accuracy of surface emg classification in dynamic contractions for the control of multifunction prostheses. Journal of Rehabilitation Research and Development, 2011, 8, 25.

    Google Scholar 

  17. Hudgins B, Parker P, Scott R N. A new strategy for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 1993, 40, 82–94.

    Article  Google Scholar 

  18. Kim K S, Choi H H, Moon C S, Mun C W. Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Current Applied Physics, 2011, 11, 740–745.

    Article  Google Scholar 

  19. Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 2012, 39, 7420–7431.

    Article  Google Scholar 

  20. Phinyomark A, Hirunviriya S, Limsakul C, Phukpattaranont P. Evaluation of EMG feature extraction for hand movement recognition based on euclidean distance and standard deviation. Proceedings of the 2010 ECTI International Conference on Electrical Engineering, Electronics, Computer, Telecommunications and Information Technology, Chiang Mai, Thailand, 2010, 856–860.

    Google Scholar 

  21. Englehart K, Hudgins B, Parker P A. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 2001, 48, 302–311.

    Article  Google Scholar 

  22. Park S H, Lee S P. Emg pattern recognition based on artificial intelligence techniques. IEEE Transactions on Rehabilitation Engineering, 1998, 6, 400–405.

    Article  Google Scholar 

  23. Khushaba R N, Kodagoda S, Takruri M, Dissanayake G. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications, 2012, 39, 10731–10738.

    Article  Google Scholar 

  24. Chen X, Wang Z J. Pattern recognition of number gestures based on a wireless surface EMG system. Biomedical Signal Processing and Control, 2012, 8, 184–192.

    Article  Google Scholar 

  25. Liu Y H, Huang H P, Weng C H. Recognition of electromyographic signals using cascaded kernel learning machine. IEEE/ASME Transactions on Mechatronics, 2007, 12, 253–264.

    Article  Google Scholar 

  26. Jolliffe I. Principal Component Analysis, Wiley Online Library, USA, 2005.

    Book  MATH  Google Scholar 

  27. Artemiadis P K, Kyriakopoulos K J. EMG-based control of a robot arm using low-dimensional embeddings. IEEE Transactions on Robotics, 2010, 26, 393–398.

    Article  Google Scholar 

  28. Chu J U, Moon I, Kim S K, Mun M S. Control of multifunction myoelectric hand using a real-time EMG pattern recognition. Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, 2005, 3511–3516.

    Google Scholar 

  29. Hargrove L J, Li G, Englehart K B, Hudgins B S. Principal components analysis preprocessing for improved classification accuracies in pattern-recognition-based myoelectric control. IEEE Transactions on Biomedical Engineering, 2009, 56, 1407–1414.

    Article  Google Scholar 

  30. Chu J U, Moon I, Lee Y J, Kim S K, Mun M S. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics, 2007, 12, 282–290.

    Article  Google Scholar 

  31. Khushaba R N, Kodagoda S, Takruri M, Dissanayake G. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications, 2012, 39, 10731–10738.

    Article  Google Scholar 

  32. Wang N F, Chen Y L, Zhang X M. The recognition of multi-finger prehensile postures using LDA. Biomedical Signal Processing and Control, 2013, 8, 706–712.

    Article  Google Scholar 

  33. Wang N F, Chen Y L, Zhang X M. Realtime recognition of multi-finger prehensile gestures. Biomedical Signal Processing and Control, 2015, 13, 262–269.

    Article  Google Scholar 

  34. Taylor C L, Schwarz R J. The anatomy and mechanics of the human hand. Artificial Limbs, 1955, 2, 22–35.

    Google Scholar 

  35. Lu Z. Practical Electromyography, People’s Medical Publishing House, Bei**g, China, 2000. (in Chinese)

    Google Scholar 

  36. Konrad P. The ABC of EMG: A Practical Introduction to Kinesiological Electromyography, 1st ed, Noraxon U.S.A. Inc., Scottsdale, USA, 2005.

    Google Scholar 

  37. Englehart K, Hudgins B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 2003, 50, 848–854.

    Article  Google Scholar 

  38. Smith L H, Hargrove L J, Lock B A, Kuiken T A. Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2011, 19, 186–192.

    Article  Google Scholar 

  39. Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-heart and Circulatory Physiology, 2000, 278, H2039–H2049.

    Article  Google Scholar 

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Correspondence to Nianfeng Wang.

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Wang, N., Lao, K. & Zhang, X. Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand. J Bionic Eng 14, 47–59 (2017). https://doi.org/10.1016/S1672-6529(16)60377-3

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