Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

1 Issue per year

CiteScore 2017: 0.33

SCImago Journal Rank (SJR) 2017: 0.104

ICV 2017: 99.90

Open Access
See all formats and pricing
More options …

A Two Layered Control Architecture for Prosthetic Grasping

Nayan M. Kakoty / Shyamanta M. Hazarika
Published Online: 2013-09-11 | DOI: https://doi.org/10.2478/pjbr-2013-0001


This paper presents a two layered control architecture - Superior hand control (SHC) followed by Local hand control (LHC) for an extreme upper limb prosthesis. The control architecture is for executing grasping operations involved in 70% of daily living activities. Forearm electromyogram actuated SHC is for recognition of user’s intended grasp. LHC control the fingers to be actuated for the recognized grasp. The finger actuation is controlled through a proportionalintegral- derivative controller customized with fingertip force sensor. LHC controls joint angles and velocities of the fingers in the prosthetic hand. Fingers in the prosthetic hand emulate the dynamic constraints of human hand fingers. The joint angle trajectories and velocity profiles of the prosthetic hand finger are in close approximation to those of the human finger

Keywords: Superior Hand Control; Local Hand Control; Dynamic constraints

  • [1] i-limb hand: Get a grip on functionality. Available at http://www.touchbionics.com/.Google Scholar

  • [2] Otto bock: Transcarpal hand with dmc plus control. Technical report. Available at <http://www.ottobock.com/>.Google Scholar

  • [3] G. A. Bekey, R. Tomovic, and I. Zeljkovic. Control architecturefor the Belgrade/USC hand, pages 136-149. Springer-Verlag, New York, 2009.Google Scholar

  • [4] J. T. Belter and A. M. Dollar. Performance characteristics of anthropomorphic prosthetic hands. In IEEE International Conf.on Rehab. Robotics, pages 921-927, Zurich, 2011.Google Scholar

  • [5] I. Carpinella, J. Jonsdottir, and M. Ferrarin. Multi-finger coordination in healthy subjects and stroke patients: a mathematical modelling approach. IEEE Transaction on Automation andControl, 8(19):1-19, 2011.Web of ScienceGoogle Scholar

  • [6] M. Carrozza, F. Vecchi, F. Sebastiani, G. Cappiello, S. Roccella†, M. Zecca, R. Lazzarini, and P. Dario. Experimental analysis of an innovative prosthetic hand with proprioceptive sensors. In IEEE International Conference on Robotics and Automa-tion, pages 2230-35, Taiwan, 2003.Google Scholar

  • [7] C. Castellini, A. E. Fiorilla, and G. Sandini. Multi-subject/daily-life activity emg-based control of mechanical hands. J. of Neuro-Engineering and Rehab., 4(6):1-11, 2009.Google Scholar

  • [8] C. Castellini, E. Fiorilla, and G. Sandini. Multi-subject/ DLA analysis of surface EMG control of mechanical hands. In Italian Bio-engineering Congress, Italy, 2008.Google Scholar

  • [9] C. Castellini and S. Patrick. Surface emg in advanced hand prosthetics. Bio. Cybernetics, 100(1):35-47, 2009.Google Scholar

  • [10] A. D. C. Chan and K. B. Englehart. Continuous myoelectric control for powered prostheses using hidden markov models. IEEETrans. on Biomed. Engineering, 52(1):121-124, 2005.PubMedGoogle Scholar

  • [11] C. Cipriani, M. Controzzi, and M. C. Carrozza. The smarthand transradial prosthesis. Journal of NeuroEngineering and Re-habilitation, 8(29):1-13, 2011.Web of ScienceGoogle Scholar

  • [12] B. Crawford, K. Miller, P. Shenoy, and R. Rao. Real-time classification of electromyographic signals for robotic control. Technical Report 2005-03-05, Dept. of Computer Science, University of Washington, 2005.Google Scholar

  • [13] S. Ferguson and G. R. Dunlop. Grasp recognition from myoelectric signals. In Proceedings of Australian Conference on Roboticsand Automation, pages 82-84, Auckland, 2002.Google Scholar

  • [14] M. Folgheraiter and G. Gini. Blackfingers an artificial hand that copies human hand in structure, size, and function. In Proc. IEEEHumanoids, MIT, Cambridge, 2000.Google Scholar

  • [15] K. Hoshino and I. Kawabuchi. Pinching at fingertips for humanoid robot hand. Robo. and Mech., 17(6):655-63, 2005.Google Scholar

  • [16] C. C. C. C.-W. Hsu and C. J. Lin. A practical guide to support vector classification, 2009.Google Scholar

  • [17] S. C. Jacobsen, E. K. Iversen, and D. F. Knutti. Design of the UTAH/MIT dextrous hand. In IEEE Int. Conf. on Robotics andAutomation, pages 1520-1532, San Francisco, 1986.Google Scholar

  • [18] R. S. Johansson and I. Birznieks. First spikes in ensembles of human tactile afferents code complex spatial fingertip events. J.of Nature Neuroscience, 7(2):170-177, 2004.PubMedGoogle Scholar

  • [19] N. M. Kakoty and S. M. Hazarika. Biomimetic design and development of a prosthetic hand: Prototype 1.0. In 15th Conf. onMach. and Mecha., pages 499-06, India, 2011.Google Scholar

  • [20] N. M. Kakoty and S. M. Hazarika. Recognition of grasp types through PCs of DWT based EMG features. In Intl. Conf. on Re-hab. Robotics, pages 478-482, Zurich, Switzerland, 2011.Google Scholar

  • [21] J. J. Kuch and T. S. Huang. Vision based hand modelling and tracking for virtual teleconferencing and telecollaboration. In IEEE/ 5th Internation Conference on Computer Vision, pages 666-71, Washington, 1995.Google Scholar

  • [22] S. W. Lee and X. Zhang. Biodynamic modeling, system identification, and variability of multi-finger movements. Journal ofBiomechanics, 40(4):3215-22, 2007.Web of ScienceGoogle Scholar

  • [23] S. Li, J. Liao, and J. T. Kwok. Wavelet-based feature extraction for microarray data classification. In IEEE Intl. Conf. on NeuralNetwork, pages 5028-5033, Canada, 2006.Web of ScienceGoogle Scholar

  • [24] S. G. Mallat and S. Zhong. Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis andMachine Intelligence, 14(7):710-732, 1992.Google Scholar

  • [25] C. Martelloni, J. Carpaneto, and S. Micera. Classification of upper arm EMG signals during object-specific grasp. In 30th Interna-tional IEEE/ EMBS Conference, Canada, 2008.Google Scholar

  • [26] R. M. Murray, Z. Li, and S. S. Sastry. A Mathematical Introduc-tion to Robotic Manipulation. CRC Press, USA, 1994.Google Scholar

  • [27] A. Phinyomark, C. Limsakul, and P. Phukpattaranont. Evaluation of wavelet function based on robust emg feature extraction. In The 7th PSU Engg. Conf., pages 277-281, 2009.Google Scholar

  • [28] M. A. Smith and J. F. Soechting. Modulation of grasping forces during object transport. J. Neuro., 93(1):137-45, 2005.PubMedGoogle Scholar

  • [29] F. Vecchi, S. Micera, M. C. Carrozza, A. M. Sabatini, and P. Dario. A sensorized glove for applications in biomechatronics and motor control. In IFESS Conf., pages 346-53, CA, 2008.Google Scholar

  • [30] M. Yoshikawa, M. Mikawa, and K. Tanaka. Real time hand motion estimation using emg signals with support vector machines. In SICE-ICASE International Joint Conference, pages 593-598, Korea, 2006.Google Scholar

  • [31] J. Zhong. PID controller tuning: A short tutorial. Technical report, 2006. 9 Google Scholar

About the article

Published Online: 2013-09-11

Published in Print: 2013-09-01

Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 4, Issue 1, Pages 1–9, ISSN (Print) 2081-4836, DOI: https://doi.org/10.2478/pjbr-2013-0001.

Export Citation

This content is open access.

Comments (0)

Please log in or register to comment.
Log in