Abstract
When developing a humanoid myo-control hand, not only the mechanical structure should be considered to afford a high dexterity, but also the myoelectric (electromyography, EMG) control capability should be taken into account to fully accomplish the actuation tasks. This paper presents a novel humanoid robotic myocontrol hand (AR hand III) which adopted an underactuated mechanism and a forearm myocontrol EMG method. The AR hand III has five fingers and 15 joints, and actuated by three embedded motors. Underactuation can be found within each finger and between the rest three fingers (the middle finger, the ring finger and the little finger) when the hand is grasping objects. For the EMG control, two specific methods are proposed: the three-fingered hand gesture configuration of the AR hand III and a pattern classification method of EMG signals based on a statistical learning algorithm — Support Vector Machine (SVM). Eighteen active hand gestures of a testee are recognized effectively, which can be directly mapped into the motions of AR hand III. An on-line EMG control scheme is established based on two different decision functions: one is for the discrimination between the idle and active modes, the other is for the recognition of the active modes. As a result, the AR hand III can swiftly follow the gesture instructions of the testee with a time delay less than 100 ms.
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Ambrose R O, Aldridge H, Askew R S, Burridge R R, Bluethmann W, Diftler M, Lovchik C, Magruder D, Rehnmark F. Robonaut: Nasa’s space humanoid. IEEE Intelligent Systems and their Applications, 2000, 15, 57–63.
Butterfass J, Grebenstein M, Liu H, Hirzinger G. DLR-Hand II: Next generation of a dextrous robot hand. Proceedings of IEEE International Conference on Robotics and Automation, Seoul, Korea, 2001, 1, 109–114.
Biagiotti L, Lotti F, Melchiorri C, Vassura G. How Far is the Human Hand? A Review on Anthropomorphic Robotic End-Effectors. University of Bologna, Italy, 2002, [2009-4-5], http://www-lar.deis.unibo.it/woda/data/deis-lar-publications/3cbd.Document.pdf
Bicchi A. Hands for dexterous manipulation and robust grasping: A difficult road toward simplicity. IEEE Transaction on Robotics and Automation, 2000, 16, 652–662.
Hollerbach J M. Workshop on the Design and Control of Dexterous Hands, MIT, 1982, [2009-4-20], http://hdl.handle.net/1721.1/5688
Carrozza M C, Cappiello G, Cavallaro E, Mieera S, Vecchi F, Dario P. Design and control of an underactuated cybernetic artificial hand. Proceedings of 2004 World Automation Congress, Seville, Spain, 2004, 1, 111–116.
Zollo L, Roccella S, Guglielmelli E, Carrozza M C, Dario P. Biomechatronic design and control of an anthropomorphic artificial hand for prosthetic and robotic applications. IEEE/ASME Transactions on Mechatronics, 2007, 12, 418–429.
Pons J L, Rocon E, Ceres R. The MANUS-HAND dextrous robotics upper limb prosthesis: Mechanical and manipulation aspects. Autonomous Robots, 2004, 16, 143–163.
Laliberte T, Gosselin C. Simulation and design of under-actuated mechanical hands. Mechanism and Machine Theory, 1998, 33, 39–57.
De Luca C J. The use of electromyography in biomechanics. Journal of Applied Biomechanics, 1997, 13, 135163.
Shimizu S, Shimojo M, Sato S, Seki Y, Takahashi A, Inukai Y, Yoshioka M. The relationship between human grip types and force distribution pattern in grasping. Proceedings of the 8th International Conference on Advanced Robotics, Monterey, CA, USA, 1997, 299–304.
Ingram J N, Körding K P, Howard I S, Wolpert D M. The statistics of natural hand movements. Experimental Brain Research, 2008, 188, 223–236.
Carrozza M C, Massa B, Micera S, Lazzarini R, Zecca M, Dario P. The development of a novel biomechatronic hand-ongoing research and preliminary results. IEEE/ASME Transactions on Mechatronics, 2002, 7, 108–114.
Okada T. Computer control of multijointed finger system for precise object-handling. IEEE Transactions on Systems, Man and Cybernetics, 1982, 12, 289–299.
Salisbury J K, Roth B. Kinematic and force analysis of articulated mechanical hands. Journal of Mechanisms, Transmissions and Automation in Design, 1983, 105, 35–41.
Melchiorri C, Vassura G. Mechanical and control features of the University of Bologna Hand version 2. Proceedings of the 1992 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1992, 1, 187–193.
Yu C, Jiang L, Huang H, Shi S. Study on the apery and drive lacking mechanism for artificial hand of disabled person. Journal of Machine Design, 2007, 9, 44–46. (in Chinese)
Huang H, Jiang L, Liu Y, Hou L, Cai H, Liu Hong. The mechanical design and experiments of HIT/DLR prosthetic hand. Proceedings of the 2006 IEEE International Conference on Robotics and Biomimetics, 2006, 2, 896–901.
Li J W, Liu H, Cai H G. On computing three-finger force-closure grasps of 2-D and 3-D objects. IEEE Transactions on Robotics and Automation, 2003, 19, 155–161.
Kyberd P J. The intelligent hand. IEE Review, 2000, 9, 31–35.
Otto Bock MYOBOCK 13E200=50 electrodes. [2009-7-20], http://www.ottobock.com/cps/rde/xchg/ob_us_en/hs.xsl/16573.html
Bitzer S, van der Smagt P. Learning EMG control of a robotic hand: Towards active prostheses. Proceedings of International Conference on Robotics and Automation, Orlando, Florida, USA, 2006, 2819–2823.
Serlin D M, Schieber M H. Morphologic regions of the multitendoned extrinsic finger muscles in the monkey forearm. Acta Anat, 1993, 146, 255–266.
Bickerton L E, Agur A M, Ashby P. Flexor digitorum superficialis: Locations of individual muscle bellies for botulinum toxin injections. Muscle Nerve, 1997, 20, 1041–1043.
Boser B E, Guyon I M, and Vapnik V N. A training algorithm for optimal margin classifiers. Proceedings of 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, USA, 1992, 144–152.
Cortes C, Vapnik V. Support-vector network. Machine Learning, 1995, 20, 273–297.
Keerthi S S, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 2003, 15, 1667–1689.
Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 2002, 13, 415–425.
Chang C, Lin C. LIBSVM: A Library for Support Vector Machines. [2009-07-20], http://www.csie.ntu.edu.tw/∼cjlin/libsvm/index.html
Zhao J, Xie Z, Jiang L, Cai H, Liu H, Hirzinger G. Levenberg-Marquardt based neural network control for a five-fingered prosthetic hand. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, 4482–4487.
Platt J. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, USA, 1999.
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Yang, Dp., Zhao, Jd., Gu, Yk. et al. An anthropomorphic robot hand developed based on underactuated mechanism and controlled by EMG signals. J Bionic Eng 6, 255–263 (2009). https://doi.org/10.1016/S1672-6529(08)60119-5
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DOI: https://doi.org/10.1016/S1672-6529(08)60119-5