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Evidence of muscle synergies during human grasping

Abstract

Motor synergies have been investigated since the 1980s as a simplifying representation of motor control by the nervous system. This way of representing finger positional data is in particular useful to represent the kinematics of the human hand. Whereas, so far, the focus has been on kinematic synergies, that is common patterns in the motion of the hand and fingers, we hereby also investigate their force aspects, evaluated through surface electromyography (sEMG). We especially show that force-related motor synergies exist, i.e. that muscle activation during grasping, as described by the sEMG signal, can be grouped synergistically; that these synergies are largely comparable to one another across human subjects notwithstanding the disturbances and inaccuracies typical of sEMG; and that they are physiologically feasible representations of muscular activity during grasping. Potential applications of this work include force control of mechanical hands, especially when many degrees of freedom must be simultaneously controlled.

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Notes

  1. Recall that from now on we will be using the dataset obtained by averaging out the sensor values over the carrying phases identified during the preprocessing phase.

References

  • Arbib M, Metta G, van der Smagt P (2008) Neurorobotics: from vision to action, Chap. 62. Springer, Berlin, pp 1453–1480

  • Atzori M, Gijsberts A, Heynen S, Mittaz-Hager AG, Deriaz O, van der Smagt P, Castellini C, Caputo B, Müller H (2012) Building the NINAPRO database: a resource for the biorobotics community. In: Proceedings of BioRob—IEEE international conference on biomedical robotics and biomechatronics, pp 1258–1265

  • Bernstein N (1967) The coordination and regulation of movements. Pergamon Press, Oxford

    Google Scholar 

  • Bicchi A, Gabiccini M, Santello M (2011) Modelling natural and artificial hands with synergies. Philos Trans R Soc London. Ser B, Biol Sci 366(1581):3153–3161. doi:10.1098/rstb.2011.0152

    Article  Google Scholar 

  • Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the 5th annual ACM workshop on computational learning theory, ACM press, pp 144–152

  • Brochier T, Spinks RL, Umiltà MA, Lemon RN (2004) Patterns of muscle activity underlying object-specific grasp by the macaque monkey. J Neurophysiol 92(3):1770–1782

    Google Scholar 

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Knowl Discov Data Min 2(2):955–974

    Google Scholar 

  • Castellini C, Fiorilla AE, Sandini G (2009) Multi-subject/daily-life activity EMG-based control of mechanical hands. J Neuroeng Rehabil 6(41). doi:10.1186/1743-0003-6-41.

  • Castellini C, Gruppioni E, Davalli A, Sandini G (2009) Fine detection of grasp force and posture by amputees via surface electromyography. JPhysiol 103(3–5):255–262. doi:10.1016/j.jphysparis.2009.08.008

    Google Scholar 

  • Castellini C, van der Smagt P (2009) Surface EMG in advanced hand prosthetics. Biol Cybern 100(1):35–47. doi:10.1007/s00422-008-0278-1

    PubMed  Article  Google Scholar 

  • Castellini C, van der Smagt P (2011) Preliminary evidence of dynamic muscular synergies in human grasping. In: Proceedings of ICAR—international conference on, advanced robotics, pp 28–33 doi:10.1109/ICAR.2011.6088612

  • Cipriani C, Antfolk C, Controzzi M, Lundborg G, Rosén B, Carrozza M, Sebelius F (2011) Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Trans Neural Syst Rehabi Eng 19(3):260–270. doi:10.1109/TNSRE.2011.2108667

    Article  Google Scholar 

  • Cutkosky M (1989) On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transa Robot Autom 5(3): 269–279

    Google Scholar 

  • De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13(2):135–163

    Google Scholar 

  • De Luca CJ (2002) Surface electromyography: detection and recording. Copyright 2002 by DelSys, Inc.

  • Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2 edn. Wiley, New York

  • Grebenstein M, van der Smagt P (2008) Antagonism for a highly anthropomorphic hand-arm system. Adv Robot 22(1):39–55. doi:10.1163/156855308X291836

    Google Scholar 

  • Grinyagin IV, Biryukova EV, Maier MA (2005) Kinematic and dynamic synergies of human precision-grip movements. J Neurophysiol 94(4):2284–2294

    PubMed  Article  CAS  Google Scholar 

  • Nawab HS, Wotiz RP, De Luca CJ (2008) Decomposition of indwelling EMG signals. J Appl Physiol 105:700–710

    PubMed  Article  Google Scholar 

  • Holdefer RN, Miller LE (2002) Primary motor cortical neurons encode functional muscle synergies. Exp Brain Res 146(2):233–243

    PubMed  Article  CAS  Google Scholar 

  • Kang SB, Ikeuchi K (1993) A grasp abstraction hierarchy for recognition of grasping tasks from observation. Proc. IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Yokohama

  • Lang CE, Schieber MH (2004) Human finger independence: limitations due to passive mechanical coupling versus active neuromuscular control. J Neurophysiol 92(5):2802–2810

    PubMed  Article  Google Scholar 

  • Mussa-Ivaldi FA, Giszter SF, Bizzi E (1994) Linear combinations of primitives in vertebrate motor control. Proc Natl Acad Sci USA 91(16):7534–7538

    Google Scholar 

  • Nilsson J (2004) Implementing a continuously updating, high-resolution time provider for windows. The MSDN Magazine

  • Orabona F, Castellini C, Caputo B, Fiorilla E, Sandini G (2009) Model adaptation with least-squares SVM for hand prosthetics. In: Proceedings of ICRA—International Conference on Robotics and Automation, pp 2897–2903 doi:10.1109/ROBOT.2009.5152247.

  • Overduin SA, d’Avella A, Roh J, Bizzi E (2008) Modulation of muscle synergy recruitment in primate grasping. J Neurosci 28(4):880–892

    Google Scholar 

  • Santello M, Flanders M, Soechting JF (1998) Postural synergies for tool use. Neuroscience 17:10105–10115

    Google Scholar 

  • Santello M, Flanders M, Soechting JF (2002) Patterns of hand motion during grasping and the influence of sensory guidance. Neuroscience 22(4):1426–1435

    PubMed  CAS  Google Scholar 

  • Sebelius FCP, Rosén BN, Lundborg GN (2005) Refined myoelectric control in below-elbow amputees using artificial neural networks and a data glove. J Hand Surg 30(4):780–789

    Google Scholar 

  • Stillfried G, van der Smagt P (2010) Movement model of a human hand based on magnetic resonance imaging (MRI). International Conference on Applied Bionics and Biomechanics (ICABB), Venice

    Google Scholar 

  • Takei T, Seki K (2010) Spinal interneurons facilitate coactivation of hand muscles during a precision grip task in monkeys. J Neurosci 30(50):17041–17050

    Google Scholar 

  • Tenore FV, 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(5): 1427–1434

    PubMed  Article  Google Scholar 

  • Tommasi T, Orabona F, Castellini C, Caputo B (2012) Improving control of dexterous hand prostheses using adaptive learning. IEEE Transactions on Robotics. doi:10.1109/TRO.2012.2226386

  • Tsuji H, Ichinobe H, Ito K, Nagamachi M (1993) Discrimination of forearm motions from emg signals by error back propagation typed neural network using entropy. IEEE Trans Soc Instrum Control Eng 29(10):1213–1220

    Google Scholar 

  • Vapnik VN (1998) Stat Learn Theory. Wiley, New York

    Google Scholar 

  • Vogel J, Castellini C, van der Smagt P (2011) EMG-based teleoperation and manipulation with the DLR LWR-III. In: Proceedings of IROS —international conference on intelligent robots and systems, pp 672–678. doi:10.1109/IROS.2011.6048345

  • Wimböck 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, pp 973–979

  • Zecca M, Micera S, Carrozza MC, Dario P (2002) Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit Rev Biomed Eng 30(4–6):459–485

    PubMed  Article  CAS  Google Scholar 

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Acknowledgments

This work is partially supported by the European FP7-Project THE Hand Embodied (FP7-IST-248257) and by the Swiss National Science Foundation Sinergia project #132700, Ninapro (Non-Invasive Adaptive Prosthetics). The authors would also like to thank Mr. Johann Buchner of the DLR for building some of the electronics involved in the setup. Legal compliance the authors declare that the experiment described in this paper complies with the current relevant German laws.

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Correspondence to Claudio Castellini.

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Castellini, C., van der Smagt, P. Evidence of muscle synergies during human grasping. Biol Cybern 107, 233–245 (2013). https://doi.org/10.1007/s00422-013-0548-4

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  • DOI: https://doi.org/10.1007/s00422-013-0548-4

Keywords

  • Rehabilitation robotics
  • Grasping
  • Electromyography