A Review on Upper-Limb Myoelectric Prosthetic Control

REFERENCES

• D. Dorcas and R. N. Scott , “A three state myoelectric control,” Med. & biol. Engng., Vol. 4. pp. 367–72, 1966.
   PubMedGoogle Scholar
• G. Li , “Electromyography pattern-recognition-based control of powered multifunctional upper-limb prostheses.” Adv. Appl. Electromyography . InTech, 2011.
   Google Scholar
• T. A. Kuiken , G. Li , B. A. Lock , R. D. Lipschutz , L. A. Miller , K. A. Stubblefield , and K. B. Englehart , “Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms.” Jama , Vol. 301, no. 6, 619–28, 2009.
   PubMed Web of Science ®Google Scholar
• M. Atzori and H. Müller , “Control capabilities of myoelectric robotic prostheses by hand amputees: A scientific research and market overview.” Front. Syst. Neurosci. , Vol. 9, 2015.
   PubMed Web of Science ®Google Scholar
• L. J. Hargrove , B. A. Lock , and A. M. Simon , “Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation.” in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE , Osaka, 2013, pp. 1599–1602. IEEE.
   Google Scholar
• F. Cordella , A. L. Ciancio , R. Sacchetti , A. Davalli , A. G. Cutti , E. Guglielmelli , and L. Zollo , “Literature review on needs of upper limb prosthesis users.” Front. Neurosci. Vol. 10, 2016.
   Web of Science ®Google Scholar
• N. Jiang , et al. “Myoelectric control of artificial limbs: is there the need for a change of focus?,” IEEE Signal Process. Mag. Vol. 152, pp. 1–4. 2012. doi: 10.1109/MSP. 2012.2203480. (2008): 1221–1224.
   Google Scholar
• D. Farina , N. Jiang , H. Rehbaum , A. Holobar , B. Graimann , H. Dietl , O. C. Aszmann , “The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 22, no. 4, pp. 797–809, July 2014.
   PubMed Web of Science ®Google Scholar
• K. Nazarpour , C. Cipriani , D. Farina , and T. Kuiken , “Guest editorial: Advances in control of multi-functional powered upper-limb prostheses,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 22, no. 4, pp. 711–5, 2014.
   PubMedGoogle Scholar
• N. Jiang , H. Rehbaum , I. Vujaklija , B. Graimann and D. Farina , “Intuitive, Online, Simultaneous, and Proportional Myoelectric Control Over Two Degrees-of-Freedom in Upper Limb Amputees,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 22, no. 3, pp. 501–10, May 2014.
   PubMed Web of Science ®Google Scholar
• L. H. Smith , T. A. Kuiken , and L. J. Hargrove , “Evaluation of linear regression simultaneous myoelectric control using intramuscular EMG,” IEEE Trans. Biomed. Eng., Vol. 63, no. 4, pp. 737–46, April 2016.
   PubMed Web of Science ®Google Scholar
• D. Dorcas and R. N. Scott , “Improved myoelectric control systems,” Med. & biol. Engng. , Vol. 4. pp. 367–72, 1970.
   Google Scholar
• B. Hudgins , P. Parker , and R. N. Scott , “A new strategy for multifunction myoelectric control,” IEEE Trans. Biomed. Eng. , Vol. 40, no. 1, pp. 82–94, Jan. 1993.
   PubMed Web of Science ®Google Scholar
• M. A. Oskoei and H. Hu , “Myoelectric control systems—A survey.” Biomed. Signal Proc. Control. , Vol. 2.4, pp. 275–94, 2007.
   Web of Science ®Google Scholar
• E. Scheme , K. Englehart , and B. Hudgins , “A one-versus-one classifier for improved robustness of myoelectric control.” in Proc. 18th Congr. Int. Soc. Electrophysiol. Kinesiol, 2010.
   Google Scholar
• M. Zecca , S. Micera , M. C. Carrozza , and P. Dario . “Control of multifunctional prosthetic hands by processing the electromyographic signal.” Crit. Rev. Biomed. Eng. , Vol. 30, no. 4-6, 2002.
   PubMedGoogle Scholar
• L. J. Hargrove , K. Englehart , and B. Hudgins , “A comparison of surface and intramuscular myoelectric signal classification,” IEEE Trans. Biomed. Eng. , Vol. 54, no. 5, pp. 847–3, May 2007.
   PubMed Web of Science ®Google Scholar
• A. Fougner , Ø. Stavdahl , P. J. Kyberd , Y. G. Losier , and P. A. Parker , “Control of upper limb prostheses: Terminology and proportional myoelectric control—a review,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 20, no. 5, pp. 663–77, Sept. 2012.
   PubMed Web of Science ®Google Scholar
• D. Graupe and W. K. Cline , “Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes,” IEEE Trans. Syst., Man, Cybern. , Vol. SMC-5, no. 2, pp. 252–9, Mar. 1975.
   Google Scholar
• P. C. Doerschuk , D. E. Gustafon , and A. S. Willsky , “Upper extremity limb function discrimination using EMG signal analysis,” IEEE Trans. Biomed. Eng. , Vol. BME-30, no. 1, pp. 18–29, Jan. 1983.
   Web of Science ®Google Scholar
• G. Daniel , J. Salahi , and D. S. Zhang . “Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses.” J. Biomed. Eng. , Vol. 7.1, pp. 18–29, 1985.
   Google Scholar
• K. Subramaniam , M. P. Paulraj , and B. S. Divya . “EEG based hearing states detection using AR modeling techniques.” Biomedical Engineering and Sciences (IECBES), 2016 IEEE EMBS Conference on. IEEE, Kuala Lumpur, 2016, pp. 513–21.
   Google Scholar
• M. F. Kelly , P. A. Parker , and R. N. Scott , “The application of neural networks to myoelectric signal analysis: A preliminary study,” IEEE Trans. Biomed. Eng. , Vol. 37, no. 3, pp. 221–30, Mar. 1990.
   PubMed Web of Science ®Google Scholar
• W.-J. Kang , J.-R. Shiu , C.-K. Cheng , J.-S. Lai , H.-W. Tsao , and T.-S. Kuo , “The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition [movements classification],” IEEE Trans. Biomed. Eng. , Vol. 42, no. 8, pp. 777–85, Aug. 1995.
   PubMed Web of Science ®Google Scholar
• H. Bernard , K. Englehart , P. A. Parker , and R. N. Scott . “A microprocessor-based multifunction myoelectric control system.” 23rd Canadian Med. and Bio. Eng. Society Conf., 1997, pp. 138–9.
   Google Scholar
• K. Englehart , B. Hudgins , P. A. Parker , and M. Stevenson , “Classification of the myoelectric signal using time-frequency based representations.” Med. Eng. & Phys., Vol. 21, no. 6, pp. 431–38, 1999.
   PubMed Web of Science ®Google Scholar
• K. Englehart , B. Hudgin , and P. A. Parker , “A wavelet-based continuous classification scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng. , Vol. 48, no. 3, pp. 302–11, Mar. 2001.
   PubMed Web of Science ®Google Scholar
• K. Englehart and B. Hudgins , “A robust, real-time control scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng. , Vol. 50, no. 7, pp. 848–54, July 2003.
   PubMed Web of Science ®Google Scholar
• G. Wang , Z. Wang , W. Chen , and J. Zhuang “Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.” Med. Bio. Eng. Comput. , Vol. 44, no. 10, 865–72, 2006.
   PubMed Web of Science ®Google Scholar
• S. C. Saxena and A. K. Wadhwani . “A comparative study of the techniques for decomposition of EMG signals.” IETE J. Res. , Vol. 50.1, pp. 87–102, 2004.
   Web of Science ®Google Scholar
• G. Dişken , Z. Tüfekçi , L. Saribulut , and U. Çevik . “A review on feature extraction for speaker recognition under degraded conditions.” IETE Tech. Rev. , Vol. 34, no. 3, pp. 321–32, 2017.
   Web of Science ®Google Scholar
• Y. Huang , K. B. Englehart , B. Hudgins , and A. D. C. Chan , “A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses,” IEEE Trans. Biomed. Eng. , Vol. 52, no. 11, pp. 1801–11, Nov. 2005.
   PubMed Web of Science ®Google Scholar
• M. A. Oskoei and H. Hu , “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” IEEE Trans. Biomed. Eng. , Vol. 55, no. 8, pp. 1956–65, Aug. 2008.
   PubMed Web of Science ®Google Scholar
• A. Sridhar Poosapadi , and D. K. Kumar , “Decoding subtle forearm flexions using fractal features of surface electromyogram from single and multiple sensors,” J. Neuroengineering Rehabilit. , Vol. 7.1, pp. 53, 2010.
   Web of Science ®Google Scholar
• A. Phinyomark , P. Phukpattaranont and C. Limsakul , “Fractal analysis features for weak and single-channel upper-limb EMG signals.” Expert Syst. Appl. , Vol. 39, no. 12, pp. 11156–63, 2012.
   Web of Science ®Google Scholar
• F. Kristin , J. Fernandez , R. Abramczyk , M. Novy , and D. Atkins . “Applying genetic programming to control of an artificial arm,” Myoelect. Symp. , 1997.
   Google Scholar
• S.-H. Park and S.-P. Lee , “EMG pattern recognition based on artificial intelligence techniques,” IEEE Trans. Rehab. Eng. , Vol. 6, no. 4, pp. 400–5, Dec. 1998.
   PubMedGoogle Scholar
• K., Jangwoo , S. Lee , C. Shin , Y. Jang , and S. Hong . “Signal hybrid HMM-GA-MLP classifier for continuous EMG classification purpose.” in Eng. in Med. and Biology Society, 1998. Proceedings of the 20th Annual International Conf. of the IEEE , Vol. 3, IEEE, 1998, pp. 1404–7.
   Google Scholar
• F. H. Y. Chan , Y.-S. Yang , F. K. Lam , Y.-T. Zhang , and P. A. Parker , “Fuzzy EMG classification for prosthesis control,” IEEE Trans. Rehab. Eng. , Vol. 8, no. 3, pp. 305–11, Sep. 2000.
   PubMedGoogle Scholar
• A. B. Ajiboye and R. F. Weir , “A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 13, no. 3, pp. 280–91, Sept. 2005.
   PubMed Web of Science ®Google Scholar
• A. D. C. Chan and K. B. Englehart , “Continuous classification of myoelectric signals for powered prostheses using gaussian mixture models,” in Proceedings of the 25th Annual Intl. Conference of the IEEE Eng. in Med. and Biology Society , 2003, pp. 2841–4, Vol. 3.
   Google Scholar
• A. D. C. Chan , and K. B. Englehart , “Continuous myoelectric control for powered prostheses using hidden Markov models,” IEEE Trans. Biomed. Eng. , Vol. 52, no. 1, pp. 121–4, Jan. 2005.
   PubMed Web of Science ®Google Scholar
• B. Karlik , M. Tokhi , and M. Alci , “A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis,” IEEE Trans. Biomed. Eng. , Vol. 50, no. 11, pp. 1255–61, Nov. 2003.
   PubMed Web of Science ®Google Scholar
• J. Sumit Soman and S. Chandra , “The Twin SVM minimizes the total risk,” Pattern Recog. Big. Data. , Vol. 395, 2016.
   Google Scholar
• S. P. Arjunan , D. K. Kumar , and G. R. Naik , “A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology , Buenos Aires , 2010, pp. 4821–4.
   Google Scholar
• G. R. Naik , D. K. Kumar , and Jayadeva , “Twin SVM for gesture classification using the surface electromyogram,” IEEE Trans. Inform. Technol. Biomed. , Vol. 14, no. 2, pp. 301–8, Mar. 2010.
   PubMedGoogle Scholar
• S. Soman , Jayadeva, S. Arjunan , and D. K. Kumar , “Improved sEMG signal classification using the Twin SVM,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , Budapest , 2016, pp. 004507–12.
   Google Scholar
• E. Scheme MSc , P. Eng , and P. Englehart PhD . “Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use.” J. Rehabilit. Res. Develop. , Vol. 48.6, pp. 643, 2011.
   Web of Science ®Google Scholar
• A. D. Roche , H. Rehbaum , D. Farina , and O. C. Aszmann . “Prosthetic myoelectric control strategies: a clinical perspective.” Current Surg. Reports , Vol. 3, no. 2, pp. 1–11, 2014.
   Google Scholar
• C. Hargrove , Claudio, et al. “Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography.” Front. Neurorob. , Vol. 8, 2014.
   PubMed Web of Science ®Google Scholar
• A. J. Young , et al. “A new hierarchical approach for simultaneous control of multi-joint powered prostheses,” in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Confere nce on . IEEE, Rome, 2012.
   Google Scholar
• A. Fougner , E. Scheme , A. D. C. Chan , K. Englehart and Ø. Stavdahl , “Resolving the limb position effect in myoelectric pattern recognition,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 19, no. 6, pp. 644–51, Dec. 2011.
   PubMed Web of Science ®Google Scholar
• A. H. Al-Timemy , R. N. Khushaba , G. Bugmann , and J. Escudero , “Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees,” IEEE Trans. Neural Syst. Rehabil. Eng., Vol. 24, no. 6, pp. 650–61, June 2016.
   PubMed Web of Science ®Google Scholar
• M. M. C. Vidovic , H. J. Hwang , S. Amsüss , J. M. Hahne , D. Farina , and K. R. Müller , “Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 24, no. 9, pp. 961–70, Sept. 2016.
   PubMed Web of Science ®Google Scholar
• J. W. Sensinger , B. A. Lock , and T. A. Kuiken , “Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 17, no. 3, pp. 270–8, June 2009.
   PubMed Web of Science ®Google Scholar
• N. Jiang , K. B. Englehart , and P. A. Parker , “Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal,” IEEE Trans. Biomed. Eng. , Vol. 56, no. 4, pp. 1070–80, Apr. 2009.
   PubMed Web of Science ®Google Scholar
• N. Jiang , K. Englehart , and P. Parker , “Estimating forces at multiple degrees of freedom from surface EMG using non-negative matrix factorization for myoelectric control,” in 2008 First Intl. Symposium on Applied Sciences on Biomed. and Commun. Tech. , Aalborg , 2008, pp. 1–5.
   Google Scholar
• J. L. G. Nielsen , S. Holmgaard , N. Jiang , K. Englehart , D. Farina , and P. Parker . “Enhanced EMG signal processing for simultaneous and proportional myoelectric control.” in Engineering in Medicine and Biology Society, EMBC 2009. Annual Internatio nal Conference of the IEEE , IEEE, Minneapolis, MN, 2009, pp. 4335–8.
   Google Scholar
• S. Holmgaard and J. L. G. Nielsen , “A novel methodology for simultaneous and proportional force control of multifunction myoelectric upper limb prostheses, using the contralateral limb of unilateral amputees.” in Department of Health Science and Technology, Aalborg University, Tech. Rep, 2009.
   Google Scholar
• J. L. G. Nielsen , S. Holmgaard , N. Jiang , K. B. Englehart , D. Farina , and P. A. Parker , “Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training,” IEEE Trans. Biomed. Eng. , Vol. 58, no. 3, pp. 681–8, Mar. 2011.
   PubMed Web of Science ®Google Scholar
• S. Muceli and D. Farina , “Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 20, no. 3, pp. 371–8, May 2012.
   PubMed Web of Science ®Google Scholar
• L. H. Smith , T. A. Kuiken , and L. J. Hargrove . “Real-time simultaneous and proportional myoelectric control using intramuscular EMG.” J. Neural Eng. , Vol. 11.6, pp. 066013, 2016.
   Web of Science ®Google Scholar
• A. Ameri , E. J. Scheme , E. N. Kamavuako , K. B. Englehart , and P. A. Parker , “Real-Time, simultaneous myoelectric control using force and position-based training paradigms,” IEEE Trans. Biomed. Eng., Vol. 61, no. 2, pp. 279–87, Feb. 2014.
   PubMed Web of Science ®Google Scholar
• J. M. Hahne et al. , “Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control,” IEEE Trans. Neural Syst. Rehabil. Eng. , Vol. 22, no. 2, pp. 269–79, March 2014.
   PubMed Web of Science ®Google Scholar
• L. H. Smith , T. A. Kuiken , and L. J. Hargrove . “Linear regression using intramuscular EMG for simultaneous myoelectric control of a wrist and hand system.” in Neural Engineering (NER), 2015 7th International IEEE/EMBS Confere nce on , IEEE, Montpellier, 2015, pp. 619–622.
   Google Scholar