A Two Layered Control Architecture for Prosthetic Grasping

Nayan M. Kakoty 1  and Shyamanta M. Hazarika 1
  • 1 School of Engineering Tezpur University Tezpur, INDIA

Abstract

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

If the inline PDF is not rendering correctly, you can download the PDF file here.

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

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

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

  • [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.

  • [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 and Control, 8(19):1-19, 2011.

  • [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.

  • [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.

  • [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.

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

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

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

  • [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.

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

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

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

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

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

  • [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.

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

  • [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.

  • [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.

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

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

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

  • [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.

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

  • [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.

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

  • [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.

  • [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.

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

OPEN ACCESS

Journal + Issues

Paladyn. Journal of Behavioral Robotics is a fully peer-reviewed, open access journal that publishes original, high-quality research works and review articles on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems. The journal is indexed in SCOPUS.

Search