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Surface EMG in advanced hand prosthetics

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One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible.

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  • Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York

    Google Scholar 

  • Bitzer S, van der Smagt P (2006) Learning EMG control of a robotic hand: towards active prostheses. In: Proceedings of ICRA, international conference on robotics and automation, Orlando, pp 2819–2823

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

    Chapter  Google Scholar 

  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining 2(2)

  • Butterfass J, Fischer M, Grebenstein M (2004) Design and experiences with DLR hand II. In: Proceedings of the world automation congress

  • Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at

  • Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines (and other kernel-based learning methods) CUP

  • CyberHand (2007) The CyberHand project.

  • De Luca CJ (1997) The use of surface electromyography in biomechanics.

  • Ekvall S, Kragić D (2005) Grasp recognition for programming by demonstration. In: Proceedings of ICRA, international conference on robotics and automation, Barcelona

  • Ferguson S, Dunlop GR (2002) Grasp recognition from myoelectric signals. In: Proceedings of the Australasian conference on robotics and automation, Auckland

  • Figueiredo M (2003) Adaptive sparseness for supervised learning. IEEE Trans Pattern Anal Mach Intell 25(9): 1150–1159

    Article  Google Scholar 

  • Huang H, Jiang L, Zhao D, Zhao J, Cai H, Liu H, Meusel P, Willberg B, Hirzinger G (2006) The development on a new biomechatronic prosthetic hand based on under-actuated mechanism. In: Proceedings of the 2006 IEEE/RSJ international conference on intelligent robots and systems, pp 3791–3796

  • i-Limb (2007) The i-Limb system.

  • Keerthi SS, Chapelle O, DeCoste D (2006) Building support vector machines with reduced classifier complexity. J Mach Learn Res 8: 1–22

    Google Scholar 

  • Lee YJ, Mangasarian OL (2001) RSVM: reduced support vector machines. In: Proceedings of the SIAM international conference on data mining

  • NIDAQ (2007) National instruments PCI-6023E data sheet.

  • OFTS (2007) SpaceControl GmbH optical force-torque sensor leaflet.

  • Orabona F, Castellini C, Caputo B, Luo J, Sandini G (2007) Indoor place recognition using online independent support vector machines. In: Proceedings of the British machine vision conference (BMVC) (to appear)

  • OttoBock (2008a) Otto Bock MYOBOCK 13E200=50 electrodes.

  • OttoBock (2008b) Otto Bock SensorHand hand prosthesis.

  • Tipping M (2000) The relevance vector machine. In: Advances in neural information processing systems, San Mateo

  • Tsuji OFT, Kaneko M, Otsuka A (2003) A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Trans Rob Autom 19(2): 210–222

    Article  Google Scholar 

  • Vijayakumar S, D’Souza A, Schaal S (2005) Incremental online learning in high dimensions. Neural Comput 17: 2602–2634

    Article  PubMed  Google Scholar 

Download references

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Correspondence to Claudio Castellini.

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This work is partially supported by the project NEURObotics, FP6-IST-001917.

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Castellini, C., van der Smagt, P. Surface EMG in advanced hand prosthetics. Biol Cybern 100, 35–47 (2009).

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