Evidence of muscle synergies during human grasping
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.
KeywordsRehabilitation robotics Grasping Electromyography
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.
- Arbib M, Metta G, van der Smagt P (2008) Neurorobotics: from vision to action, Chap. 62. Springer, Berlin, pp 1453–1480Google Scholar
- 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 1258–1265Google Scholar
- Bernstein N (1967) The coordination and regulation of movements. Pergamon Press, OxfordGoogle 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 144–152Google Scholar
- 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):1770–1782Google Scholar
- Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Knowl Discov Data Min 2(2):955–974Google 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, 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
- Cutkosky M (1989) On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transa Robot Autom 5(3): 269–279Google Scholar
- De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13(2):135–163Google Scholar
- De Luca CJ (2002) Surface electromyography: detection and recording. Copyright 2002 by DelSys, Inc.Google Scholar
- Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2 edn. Wiley, New YorkGoogle 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, YokohamaGoogle Scholar
- Mussa-Ivaldi FA, Giszter SF, Bizzi E (1994) Linear combinations of primitives in vertebrate motor control. Proc Natl Acad Sci USA 91(16):7534–7538Google Scholar
- Nilsson J (2004) Implementing a continuously updating, high-resolution time provider for windows. The MSDN MagazineGoogle Scholar
- 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):880–892Google Scholar
- Santello M, Flanders M, Soechting JF (1998) Postural synergies for tool use. Neuroscience 17:10105–10115Google 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):780–789Google 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), VeniceGoogle Scholar
- Takei T, Seki K (2010) Spinal interneurons facilitate coactivation of hand muscles during a precision grip task in monkeys. J Neurosci 30(50):17041–17050Google 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):1213–1220Google Scholar
- Vapnik VN (1998) Stat Learn Theory. Wiley, New YorkGoogle 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 973–979Google Scholar