Advertisement

Experimental Brain Research

, Volume 237, Issue 2, pp 291–311 | Cite as

Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration

  • Dapeng YangEmail author
  • Yikun Gu
  • Nitish V. Thakor
  • Hong Liu
Review

Abstract

The development of advanced and effective human–machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.

Keywords

Myoelectric signal Motion control Hand prosthesis Pattern recognition 

Abbreviations

CNS

Central nervous system

EMG

Electromyograms

ENG

Electroneurography

EEG

Electroencephalograhy

EcoG

Electrocorticography

iEMG

Intramuscular EMG

sEMG

Surface EMG

HD-EMG

High-density EMG

MU

Motor unit

DOF

Degree of freedom

MSC

Myoelectric signal control

PR

Pattern recognition

PR-MSC

PR-based MSC

R-MSC

Regression-based MSC

E-MSC

Encoding-based MSC

S-MSC

Synergy-based MSC

PCA

Principal components analysis

TMR

Targeted muscle reinnervation

CA

Classification accuracy

TPR

True positives rate

FPR

False positives rate

GUI

Graphic user interfaces

SHAP

Southampton hand assessment procedure

TD/FD

Time/frequency domain

CWT

Continuous wavelet transform

TENS

Transcutaneous electric nerve stimulus

HMI

Human–machine interface

SVM

Support vector machine

SVDD

Support vector domain description

DA

Domain adaptation

LDA

Linear discriminant analysis

CMCA

Common model component analysis

RFID

Radio frequency identification

IMU

Inertial measurement unit

Notes

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (NO. 51675123, NO.51521003) and China Scholarship Council.

References

  1. Adewuyi AA, Hargrove LJ, Kuiken TA (2016) An analysis of intrinsic and extrinsic hand muscle EMG for improved pattern recognition control. IEEE Trans Neural Syst Rehabil Eng 24:485–494CrossRefPubMedGoogle Scholar
  2. Al-Timemy AH, Khushaba RN, Bugmann G, Escudero J (2016) Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 24:650–661.  https://doi.org/10.1109/TNSRE.2015.2445634 CrossRefPubMedGoogle Scholar
  3. Ameri A, Kamavuako E, Scheme E, Englehart K, Parker P (2014a) Support vector regression for improved real-time, simultaneous myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22:1198–1209.  https://doi.org/10.1109/tnsre.2014.2323576 CrossRefPubMedGoogle Scholar
  4. Ameri A, Kamavuako EN, Scheme EJ, Englehart KB, Parker PA (2014b) Real-time, simultaneous myoelectric control using visual target-based training paradigm. Biomed Signal Process Control 13:8–14.  https://doi.org/10.1016/j.bspc.2014.03.006 CrossRefGoogle Scholar
  5. Ameri A, Scheme EJ, Kamavuako EN, Englehart KB, Parker PA (2014c) Real-time, simultaneous myoelectric control using force and position-based training paradigms. IEEE Trans Biomed Eng 61:279–287.  https://doi.org/10.1109/tbme.2013.2281595 CrossRefPubMedGoogle Scholar
  6. Anam K, Al-Jumaily A (2017) Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees. Neural Netw 85:51–68CrossRefGoogle Scholar
  7. Atzori M, Muller H (2015) Control capabilities of myoelectric robotic prostheses by hand amputees: a scientific research and market overview. Front Syst Neurosci 9:162.  https://doi.org/10.3389/fnsys.2015.00162 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Atzori M, Cognolato M, Müller H (2016) Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands. Front Neurorobotics 10:9CrossRefGoogle Scholar
  9. Belter JT, Segil J, Dollar AM, Weir RF (2013) Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. J Rehabil Res Dev 50:599–618CrossRefPubMedGoogle Scholar
  10. Betthauser JL, Hunt CL, Osborn LE, Masters MR, Levay G, Kaliki RR, Thakor NV (2017) Limb position tolerant pattern recognition for myoelectric prosthesis control with adaptive sparse representations from extreme learning. IEEE Trans Biomed Eng PP:1–1.  https://doi.org/10.1109/TBME.2017.2719400 CrossRefGoogle Scholar
  11. Biddiss E, Chau T (2007) Upper-limb prosthetics: critical factors in device abandonment. Am J Phys Med Rehabil 86:977–987CrossRefPubMedGoogle Scholar
  12. Brown CY, Asada HH (2007) Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis. In: The 2007 IEEE/RSJ international conference on intelligent robots and systems, San Diego, CA, USAGoogle Scholar
  13. Bullock IM, Feix T, Dollar AM (2015) The Yale human grasping dataset: grasp, object, and task data in household and machine shop environments. Int J Robot Res 34:251–255CrossRefGoogle Scholar
  14. Castellini C, van der Smagt P (2009) Surface EMG in advanced hand prosthetics. Biol Cybern 100:35–47CrossRefPubMedGoogle Scholar
  15. Castellini C, van der Smagt P (2013) Evidence of muscle synergies during human grasping. Biol Cybern 107:233–245.  https://doi.org/10.1007/s00422-013-0548-4 CrossRefPubMedGoogle Scholar
  16. Castellini C, Fiorilla AE, Sandini G (2009) Multi-subject/daily-life activity EMG-based control of mechanical hands. J Neuroeng Rehabil 6:1–11.  https://doi.org/10.1186/1743-0003-6-41 CrossRefGoogle Scholar
  17. Castellini C, Artemiadis P, Wininger M et al (2014a) Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography. Front Neurorobotics 8:22CrossRefGoogle Scholar
  18. Castellini C, Artemiadis P, Wininger M et al (2014b) Proceedings of the first workshop on peripheral machine interfaces: going beyond traditional surface electromyography. Front Neurorobotics 8:21–17  https://doi.org/10.3389/fnbot.2014.00022 CrossRefGoogle Scholar
  19. Catalano MG, Grioli G, Farnioli E, Serio A, Piazza C, Bicchi A (2014) Adaptive synergies for the design and control of the Pisa/IIT SoftHand. Int J Robot Res 33:768–782.  https://doi.org/10.1177/0278364913518998 CrossRefGoogle Scholar
  20. Celadon N, Došen S, Binder I, Ariano P, Farina D (2016) Proportional estimation of finger movements from high-density surface electromyography. J Neuroeng Rehabil 13:73CrossRefPubMedPubMedCentralGoogle Scholar
  21. Chen X, Zhang D, Zhu X (2013) Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control. J Neuroeng Rehabil 10:44CrossRefPubMedPubMedCentralGoogle Scholar
  22. Cloutier A, Yang J (2013) Control of hand prostheses: a literature review. In: ASME 2013 international design engineering technical conferences and computers and information in engineering conference, Volume 6A: 37th mechanisms and robotics conference. ASME, Portland, Oregon, USA, p V06AT07A016Google Scholar
  23. Daley H, Englehart K, Hargrove L, Kuruganti U (2012) High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. J Electromyogr Kinesiol 22:478–484.  https://doi.org/10.1016/j.jelekin.2011.12.012 CrossRefPubMedGoogle Scholar
  24. Dalley SA, Varol HA, Goldfarb M (2012) A method for the control of multigrasp myoelectric prosthetic hands. IEEE Trans Neural Syst Rehabil Eng 20:58–67CrossRefPubMedGoogle Scholar
  25. Earley EJ, Hargrove LJ (2016) The effect of wrist position and hand-grasp pattern on virtual prosthesis task performance. In: 2016 6th IEEE international conference on biomedical robotics and biomechatronics (BioRob). IEEE, pp 542–547Google Scholar
  26. Earley EJ, Hargrove LJ, Kuiken TA (2016) Dual window pattern recognition classifier for improved partial-hand prosthesis control. Front Neurosci 10:58.  https://doi.org/10.3389/fnins.2016.00058 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Elliott JM, Connolly K (1984) A classification of manipulative hand movements. Dev Med Child Neurol 26:283–296CrossRefPubMedGoogle Scholar
  28. Englehart K, Hudgins B (2003) A Robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 50:848–854CrossRefPubMedGoogle Scholar
  29. Fang YF, Hettiarachchi N, Zhou DL, Liu HH (2015) Multi-modal sensing techniques for interfacing hand prostheses: a review. IEEE Sens J 15:6065–6076.  https://doi.org/10.1109/jsen.2015.2450211 CrossRefGoogle Scholar
  30. Farina D, Aszmann O (2014) Bionic limbs: clinical reality and academic promises. Sci Transl Med 6:257ps212–257ps212CrossRefGoogle Scholar
  31. Farina D, Holobar A, Merletti R, Enoka RM (2010) Decoding the neural drive to muscles from the surface electromyogram. Clin Neurophysiol 121:1616–1623.  https://doi.org/10.1016/j.clinph.2009.10.040 CrossRefPubMedGoogle Scholar
  32. Farina D, Ning J, Rehbaum H, Holobar A, Graimann B, Dietl H, Aszmann OC (2014) 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. 22:797–809.  https://doi.org/10.1109/TNSRE.2014.2305111 CrossRefPubMedGoogle Scholar
  33. Farrell TR, Weir RF (2007) The optimal controller delay for myoelectric prostheses. IEEE Trans Neural Syst Rehabil Eng 15:111–118CrossRefPubMedGoogle Scholar
  34. Feix T, Romero J, Schmiedmayer H-B, Dollar AM, Kragic D (2015) The GRASP taxonomy of human grasp types. J Mech Robot.  https://doi.org/10.1115/1111.403240 CrossRefGoogle Scholar
  35. Ficuciello F, Palli G, Melchiorri C, Siciliano B (2011) Experimental evaluation of postural synergies during reach to grasp with the UB Hand IV. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, San Francisco, CA, USAGoogle Scholar
  36. Fougner A, Scheme E, Chan AD, Englehart K, Stavdahl O (2011) Resolving the limb position effect in myoelectric pattern recognition. IEEE Trans Neural Syst Rehabil Eng 19:644–651.  https://doi.org/10.1109/tnsre.2011.2163529 CrossRefPubMedGoogle Scholar
  37. Fougner A, Stavdahl O, Kyberd PJ, Losier YG, Parker PA (2012) Control of upper limb prostheses: terminology and proportional myoelectric control: a review. IEEE Trans Neural Syst Rehabil Eng 20:663–677.  https://doi.org/10.1109/tnsre.2012.2196711 CrossRefPubMedGoogle Scholar
  38. Geng YJ, Zhou P, Li GL (2012) Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees. J Neuroeng Rehabil 9:74.  https://doi.org/10.1186/1743-0003-9-74 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Geng W, Du Y, Jin W, Wei W, Hu Y, Li J (2016) Gesture recognition by instantaneous surface EMG images. Sci Rep 6:36571CrossRefPubMedPubMedCentralGoogle Scholar
  40. Hahne JM, BieBmann F, Jiang N et al (2014) Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng 22:269–279.  https://doi.org/10.1109/TNSRE.2014.2305520 CrossRefPubMedGoogle Scholar
  41. Hargrove L, Losier Y, Lock B, Englehart K, Hudgins B (2007a) A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In: Engineering in medicine and biology society, 2007. EMBS 2007. 29th annual international conference of the IEEE, pp 4842–4845Google Scholar
  42. Hargrove LJ, Englehart K, Hudgins B (2007b) A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans Biomed Eng 54:847–853CrossRefPubMedGoogle Scholar
  43. Hargrove L, Englehart K, Hudgins B (2008) A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed Signal Process Control 3:175–180.  https://doi.org/10.1016/j.bspc.2007.11.005 CrossRefGoogle Scholar
  44. He J, Zhang D, Jiang N, Sheng X, Farina D, Zhu X (2015a) User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control. J Neural Eng 12:046005.  https://doi.org/10.1088/1741-2560/12/4/046005 CrossRefPubMedGoogle Scholar
  45. He J, Zhang D, Sheng X, Li S, Zhu X (2015b) Invariant Surface EMG feature against varying contraction level for myoelectric control based on muscle coordination. IEEE J Biomed Health Inform 19:874–882.  https://doi.org/10.1109/jbhi.2014.2330356 CrossRefPubMedGoogle Scholar
  46. Hotson G, McMullen DP, Fifer MS et al (2016) Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject. J Neural Eng 13:026017CrossRefPubMedPubMedCentralGoogle Scholar
  47. Huang H, Zhang F, Sun YL, He HB (2010) Design of a robust EMG sensing interface for pattern classification. J Neural Eng 7  https://doi.org/10.1088/1741-2560/7/5/056005
  48. Huang Q, Yang D, Jiang L, Zhang H, Liu H, Kotani K (2017) A Novel unsupervised adaptive learning method for long-term electromyography (EMG) pattern recognition. Sensors 17:1370CrossRefGoogle Scholar
  49. Hudgins B, Parker P, Scott RN (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40:82–94CrossRefPubMedGoogle Scholar
  50. Ison M, Artemiadis P (2014) The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control. J Neural Eng 11:051001.  https://doi.org/10.1088/1741-2560/11/5/051001 CrossRefPubMedGoogle Scholar
  51. Jebsen RH, Taylor N, Trieschmann R, Trotter M, Howard L (1969) An objective and standardized test of hand function. Arch Phys Med Rehabil 50(6):p 311, 50:311–319PubMedGoogle Scholar
  52. Jenkins OC, Mataric MJ (2002) Deriving action and behavior primitives from human motion data. In: IEEE/RSJ international conference on intelligent robots and systems, vol 3, pp 2551–2556 vol.2553Google Scholar
  53. Jiang N, Muceli S, Graimann B, Farina D (2013) Effect of arm position on the prediction of kinematics from EMG in amputees. Med Biol Eng Compu 51:143–151.  https://doi.org/10.1007/s11517-012-0979-4 CrossRefGoogle Scholar
  54. Jiang L, Huang Q, Zhao J, Yang D, Fan S, Liu H (2014) Noise cancellation for electrotactile sensory feedback of myoelectric forearm prostheses. In: 2014 IEEE international conference on information and automation, ICIA 2014, July 28, 2014–July 30, 2014. Institute of Electrical and Electronics Engineers Inc., Hailar, Hulunbuir, China, pp 1066–1071Google Scholar
  55. Ju Z, Liu H (2014) Human hand motion analysis with multisensory information. IEEE/ASME Trans Mechatron 19:456–466CrossRefGoogle Scholar
  56. Kamavuako E, Scheme E, Englehart K (2014) Combined surface and intramuscular EMG for improved real-time myoelectric control performance. Biomed Signal Process Control 10:102–107CrossRefGoogle Scholar
  57. Kawano S, Okumura D, Tamura H, Tanaka H, Tanno K (2009) Online learning method using support vector machine for surface-electromyogram recognition. Artif Life Robotics 13:483–487CrossRefGoogle Scholar
  58. Kent BA, Karnati N, Engeberg ED (2014) Electromyogram synergy control of a dexterous artificial hand to unscrew and screw objects. J Neuroeng Rehabil 11:41.  https://doi.org/10.1186/1743-0003-11-41 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Khushaba RN, Takruri M, Miro JV, Kodagoda S (2014) Toward limb position invariant myoelectric pattern recognition using time-dependent spectral features. Neural Netw 55:42–58CrossRefPubMedGoogle Scholar
  60. Khushaba RN, Al-Timemy A, Kodagoda S, Nazarpour K (2016) Combined influence of forearm orientation and muscular contraction on EMG pattern recognition. Expert Syst Appl 61:154–161.  https://doi.org/10.1016/j.eswa.2016.05.031 CrossRefGoogle Scholar
  61. Kuiken TA, Miller LA, Lipschutz RD et al (2007) Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study. Lancet 369:371–380CrossRefPubMedGoogle Scholar
  62. Kuiken TA, Li G, Lock BA, Lipschutz RD, Miller LA, Stubblefield KA, Englehart KB (2009) Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA J Am Med Assoc 301:619–628CrossRefGoogle Scholar
  63. Kuiken TA, Miller LA, Turner K, Hargrove LJ (2016) A comparison of pattern recognition control and direct control of a multiple degree-of-freedom transradial prosthesis. IEEE J Transl Eng Health Med 4:1–8.  https://doi.org/10.1109/JTEHM.2016.2616123 CrossRefGoogle Scholar
  64. Kyberd PJ (2011) The influence of control format and hand design in single axis myoelectric hands: assessment of functionality of prosthetic hands using the Southampton Hand Assessment Procedure. Prosthet Orthot Int 35:285–293.  https://doi.org/10.1177/0309364611418554 CrossRefPubMedGoogle Scholar
  65. Kyberd PJM, Murgia A, Gasson M, Tjerks T, Cheryl M, Chappell PH, Warwick K, Lawson SEM, Barnhill T (2009) Case studies to demonstrate the range of applications of the Southampton Hand Assessment Procedure. Br J Occup Therapy 72:212–218CrossRefGoogle Scholar
  66. Lacquaniti F, Soechting JF (1982) Coordination of arm and wrist motion during a reaching task. J Neurosci 2:399–408CrossRefPubMedGoogle Scholar
  67. Lewis S, Russold MF, Dietl H, Eugenijus K (2012) User demands for sensory feedback in upper extremity prostheses. In: 2012 IEEE international symposium on medical measurements and applications proceedings. IEEE, Budapest, Hungary, pp 1–4Google Scholar
  68. Li Z, Canny JF, Sastry SS (1989) On motion planning for dexterous manipulation. i. the problem formulation. In: Robotics and automation, 1989. Proceedings., 1989 IEEE International Conference on. IEEE, pp 775–780Google Scholar
  69. Li G, Schultz AE, Kuiken TA (2010) Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses. IEEE Trans Neural Syst Rehabil Eng 18:185–192CrossRefPubMedPubMedCentralGoogle Scholar
  70. Liarokapis MV, Artemiadis PK, Katsiaris PT, Kyriakopoulos KJ (2012) Learning human reach-to-grasp strategies: Towards EMG-based control of robotic arm-hand systems. In: IEEE international conference on robotics and automation, pp 2287–2292Google Scholar
  71. Light C, Chappell P, Kyberd P, Ellis B (1999) A critical review of functionality assessment in natural and prosthetic hands. Br J Occup Therapy 62:7–12CrossRefGoogle Scholar
  72. Light CM, Chappell PH, Kyberd PJ (2002) Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. Arch Phys Med Rehabil 83:776–783CrossRefPubMedGoogle Scholar
  73. Liu J, Zhang D, Sheng X, Zhu X (2014) Quantification and solutions of arm movements effect on sEMG pattern recognition. Biomed Signal Process Control 13:189–197.  https://doi.org/10.1016/j.bspc.2014.05.001 CrossRefGoogle Scholar
  74. Liu J, Sheng X, Zhang D, Jiang N, Zhu X (2015) Towards zero re-training for myoelectric control based on common model component analysis. IEEE Trans Neural Syst Rehabil Eng 24:444–454.  https://doi.org/10.1109/TNSRE.2015.2420654 CrossRefPubMedGoogle Scholar
  75. Liu H, Yang D, Fan S, Cai H (2016a) On the development of intrinsically-actuated, multisensory dexterous robotic hands. Robomech J 3:4.  https://doi.org/10.1186/s40648-016-0043-5 CrossRefGoogle Scholar
  76. Liu J, Sheng X, Zhang D, He J, Zhu X (2016b) Reduced daily recalibration of myoelectric prosthesis classifiers based on domain adaptation. IEEE J Biomed Health Inf 20:166–176CrossRefGoogle Scholar
  77. Liu Y, Jiang L, Yang D, Liu H (2016c) Analysis of hand and wrist postural synergies in tolerance grasping of various objects. Plos ONE 11:e0161772.  https://doi.org/10.1371/journal.pone.0161772 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Lorrain T, Jiang N, Farina D (2011) Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses. J Neuroeng Rehabil 8:1:8CrossRefGoogle Scholar
  79. Ma J, Thakor NV, Matsuno F (2015) Hand and wrist movement control of myoelectric prosthesis based on synergy. IEEE Trans Hum Mach Syst 45:74–83Google Scholar
  80. MacKenzie IS (1992) Fitts’ law as a research and design tool in human-computer interaction. Hum Comput Interact 7:91–139CrossRefGoogle Scholar
  81. Marco S, Gabriel BB, Henrik J (2013) Neural bases of hand synergies. Front Comput Neurosci 7:23Google Scholar
  82. McMullen DP, Hotson G, Katyal KD et al (2014) Demonstration of a semi-autonomous hybrid brain–machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Trans Neural Syst Rehabil Eng 22:784–796CrossRefPubMedGoogle Scholar
  83. Merad M, Montalivet Ed, Roby-Brami A, Jarrasse N (2016) Intuitive prosthetic control using upper limb inter-joint coordinations and IMU-based shoulder angles measurement: a pilot study. In: IEEE/RSJ international conference on intelligent robots and systems. IEEE, Vancouver, Canada, pp 5677–5682Google Scholar
  84. Merletti R, Holobar A, Farina D (2008) Analysis of motor units with high-density surface electromyography. J Electromyogr Kinesiol 18:879–890CrossRefPubMedGoogle Scholar
  85. Merletti R, Aventaggiato M, Botter A, Holobar A, Marateb H, Vieira TM (2010a) Advances in surface EMG: recent progress in detection and processing techniques. Crit Rev Biomed Eng 38:305–345CrossRefPubMedGoogle Scholar
  86. Merletti R, Botter A, Cescon C, Minetto M, Vieira T (2010b) Advances in surface EMG: recent progress in clinical research applications. Crit Rev Biomed Eng 38:347–379CrossRefPubMedGoogle Scholar
  87. Montagnani F, Controzzi M, Cipriani C (2015) Is it finger or wrist dexterity that is missing in current hand prosthesese. IEEE Trans Neural Syst Rehabil Eng 23:600–609CrossRefPubMedGoogle Scholar
  88. Montagnani F, Controzzi M, Cipriani C (2016) Independent long fingers are not essential for a grasping hand. Sci Rep 6:35545.  https://doi.org/10.1038/srep35545 CrossRefPubMedPubMedCentralGoogle Scholar
  89. Napier JR (1956) The prehensile movements of the human. J Bone Jt Surg 38:902–913CrossRefGoogle Scholar
  90. Ning J, Dosen S, Muller KR, Farina D (2012) Myoelectric control of artificial limbs: is there a need to change focus? IEEE Signal Process Mag 29:148–152.  https://doi.org/10.1109/msp.2012.2203480 CrossRefGoogle Scholar
  91. Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomed Signal Process Control 2:275–294CrossRefGoogle Scholar
  92. Oskoei MA, Hu H (2009) Adaptive myoelectric human-machine interface for video games. In: Proceedings of the 2009 IEEE international conference on mechatronics and automation. IEEE, Changchun, China, pp 1015–1020Google Scholar
  93. Oskoei MA, Huosheng H (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55:1956–1965CrossRefPubMedGoogle Scholar
  94. Pan L, Zhang D, Jiang N, Sheng X, Zhu X (2015) Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns. J Neuroeng Rehabil 12:1CrossRefGoogle Scholar
  95. Peerdeman B, Boere D, Witteveen H et al (2011) Myoelectric forearm prostheses: state of the art from a user-centered perspective. J Rehabil Res Dev 48:719–737.  https://doi.org/10.1682/jrrd.2010.08.0161 CrossRefPubMedGoogle Scholar
  96. Pilarski PM, Dawson MR, Degris T, Carey JP, Chan KM, Hebert JS, Sutton RS (2013) Adaptive artificial limbs: a real-time approach to prediction and anticipation. IEEE Robot Autom Mag 20:53–64CrossRefGoogle Scholar
  97. Pons JL, Ceres∗ R, Rocon∗ E et al (2005) Virtual reality training and EMG control of the MANUS hand prosthesis. Robotica 23:311–317  https://doi.org/10.1017/S026357470400133X CrossRefGoogle Scholar
  98. Rombokas E, Malhotra M, Theodorou EA, Todorov E, Matsuoka Y (2013) Reinforcement learning and synergistic control of the ACT hand. IEEE/ASME Trans Mech 18:569–577.  https://doi.org/10.1109/TMECH.2012.2219880 CrossRefGoogle Scholar
  99. Sahu OP, Balabantaray B, Mishra N, Biswal BB (2017) An integrated approach of sensors to detect grasping point for unstructured 3-D parts. Int J Eng Technol 9:84CrossRefGoogle Scholar
  100. Saxena A, Driemeyer J, Ng AY (2008) Robotic grasping of novel objects using vision. Int J Robot Res 27:157–173CrossRefGoogle Scholar
  101. Scheme E, Englehart K (2011) Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. J Rehabil Res Dev 48:643–660.  https://doi.org/10.1682/jrrd.2010.09.0177 CrossRefPubMedGoogle Scholar
  102. Scheme E, Englehart K (2013) Training strategies for mitigating the effect of proportional control on classification in pattern recognition based myoelectric control. J Prosthet Orthot 25:76–83.  https://doi.org/10.1097/JPO.0b013e318289950b CrossRefPubMedPubMedCentralGoogle Scholar
  103. Scheme EJ, Englehart KB, Hudgins BS (2011) Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Trans Biomed Eng 58:1698–1705CrossRefPubMedGoogle Scholar
  104. Scheme E, Hudgins B, Englehart K (2013) Confidence based rejection for improved pattern recognition myoelectric control. IEEE Trans Biomed Eng 60:1563–1570.  https://doi.org/10.1109/tbme.2013.2238939 CrossRefPubMedGoogle Scholar
  105. Sensinger JW, Lock BA, Kuiken TA (2009) Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Trans Neural Syst Rehabil Eng 17:270–278.  https://doi.org/10.1109/tnsre.2009.2023282 CrossRefPubMedPubMedCentralGoogle Scholar
  106. Shin S, Tafreshi R, Langari R (2016) Myoelectric pattern recognition using dynamic motions with limb position changes. In: 2016 Conference AC (ACC). IEEE, pp 4901–4906Google Scholar
  107. Smith HB (1973) Smith hand function evaluation. Am J Occup Therapy 27:244Google Scholar
  108. Smith LH, Hargrove LJ (2013) Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 4223–4226Google Scholar
  109. Smith LH, Hargrove LJ, Lock BA, Kuiken TA (2011) Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Trans Neural Syst Rehabil Eng 19:186–192.  https://doi.org/10.1109/TNSRE.2010.2100828 CrossRefPubMedGoogle Scholar
  110. Smith LH, Kuiken TA, Hargrove LJ (2014) Real-time simultaneous and proportional myoelectric control using intramuscular EMG. J Neural Eng 11:066013CrossRefPubMedPubMedCentralGoogle Scholar
  111. Stango A, Negro F, Farina D (2015) Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol. IEEE Trans Neural Syst Rehabil Eng 23:189–198CrossRefPubMedGoogle Scholar
  112. Tax D, Duin R (1999) Data domain description using support vectors. In: Verleysen M (ed) Procedings of European symposium artificial neural networks. D. Facto, Brussel, pp 251–256Google Scholar
  113. Tenore FVG, Ramos A, Fahmy A, Acharya S, Etienne-Cummings R, Thakor NV (2009) Decoding of individuated finger movements using surface electromyography. IEEE Trans Biomed Eng 56:1427–1434CrossRefPubMedGoogle Scholar
  114. Tommasi T, Orabona F, Castellini C, Caputo B (2013) Improving control of dexterous hand prostheses using adaptive learning. IEEE Trans Robot 29:207–219CrossRefGoogle Scholar
  115. Trachtenberg MS, Singhal G, Kaliki R, Smith RJ, Thakor NV (2011) Radio frequency identification—an innovative solution to guide dexterous prosthetic hands. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, pp 3511–3514Google Scholar
  116. Tucker M, Ellis R (2001) The potentiation of grasp types during visual object categorization. Vis Cognit 8:769–800CrossRefGoogle Scholar
  117. Wentink EC, Beijen SI, Hermens HJ, Rietman JS, Veltink PH (2013) Intention detection of gait initiation using EMG and kinematic data. Gait Posture 37:223–228 doi.  https://doi.org/10.1016/j.gaitpost.2012.07.013 CrossRefPubMedGoogle Scholar
  118. Wimbock T, Jahn B, Hirzinger G (2011) Synergy level impedance control for multifingered hands. In: Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ international conference on, San Francisco, CA, USA, pp 973–979Google Scholar
  119. Wurth SM, Hargrove LJ (2014) A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts’ law style assessment procedure. J Neuroeng Rehabil 11:91CrossRefPubMedPubMedCentralGoogle Scholar
  120. Xu K, Du Y, Liu H, Sheng X, Zhu X (2013) Mechanical implementation of postural synergies of an underactuated prosthetic hand. In: ICIRAGoogle Scholar
  121. Yang D, Zhao J, Gu Y et al (2009) An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals. J Bionic Eng 6:255–263CrossRefGoogle Scholar
  122. Yang D, Zhao J, Jiang L, Liu H (2012) Dynamic hand motion recognition based on transient and steady-state EMG signals. Int J Humanoid Rob 9:11250007Google Scholar
  123. Yang D, Jiang L, Liu R, Liu H (2013) Adaptive learning of multi-finger motion recognition based on support vector machine. In: 2013 IEEE international conference on robotics and biomimetics, ROBIO 2013, December 12, 2013 - December 14, 2013. IEEE Computer Society, Shenzhen, China, pp 2231–2238Google Scholar
  124. Yang D, Gu Y, Liu R, Liu H (2014a) Dexterous motion recognition for myoelectric control of multifunctional transradial prostheses. Adv Robot 28:1533–1543.  https://doi.org/10.1080/01691864.2014.957723 CrossRefGoogle Scholar
  125. Yang D, Jiang L, Huang Q, Liu R, Liu H (2014b) Experimental study of an EMG-controlled 5-DOF anthropomorphic prosthetic hand for motion restoration. J Intell Rob Syst 76:427–441.  https://doi.org/10.1007/s10846-014-0037-6 CrossRefGoogle Scholar
  126. Yang D, Gu Y, Jiang L, Osborn L, Liu H (2017a) Dynamic training protocol improves the robustness of PR-based myoelectric control. Biomed Signal Process Control 31:249–256.  https://doi.org/10.1016/j.bspc.2016.08.017 CrossRefGoogle Scholar
  127. Yang D, Yang W, Huang Q, Liu H (2017b) Classification of multiple finger motions during dynamic upper limb movements. IEEE J Biomed Health Inform 21:134–141.  https://doi.org/10.1109/JBHI.2015.2490718 CrossRefPubMedGoogle Scholar
  128. Yang W, Yang D, Liu Y, Liu H (2018) A 3-DOF hemi-constrained wrist motion/force detection device for deploying simultaneous myoelectric control. Med Biol Eng Compu 56:1669–1681CrossRefGoogle Scholar
  129. Yinfeng F, Honghai L (2014) Robust sEMG electrodes configuration for pattern recognition based prosthesis control. In: 2014 IEEE international conference on systems, man and cybernetics (SMC). pp 2210–2215Google Scholar
  130. Young AJ, Hargrove LJ, Kuiken TA (2011) The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift. IEEE Trans Biomed Eng 58:2537–2544CrossRefPubMedPubMedCentralGoogle Scholar
  131. Young AJ, Hargrove LJ, Kuiken TA (2012) Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Trans Biomed Eng 59:645–652.  https://doi.org/10.1109/tbme.2011.2177662 CrossRefPubMedGoogle Scholar
  132. Zecca M, Micera S, Carrozza MC, Dario P (2002) Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit Rev Biomed Eng 30:459–485CrossRefPubMedGoogle Scholar
  133. Zhang X, Zhou P (2012) High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans Biomed Eng 59:1649–1657.  https://doi.org/10.1109/tbme.2012.2191551 CrossRefPubMedGoogle Scholar
  134. Zhang Y, Wang Z, Zhang Z, Fang Y, Liu H (2016) Comparison of online adaptive learning algorithms for myoelectric hand control. In: 2016 9th international conference on human system interactions (HSI). IEEE, pp 69–75Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dapeng Yang
    • 1
    • 2
    Email author
  • Yikun Gu
    • 1
  • Nitish V. Thakor
    • 3
  • Hong Liu
    • 1
  1. 1.State Key Laboratory of Robotics and SystemHarbin Institute of TechnologyHarbinChina
  2. 2.Artificial Intelligence LaboratoryHarbin Institute of TechnologyHarbinChina
  3. 3.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations