Literature DB >> 12739757

Control of multifunctional prosthetic hands by processing the electromyographic signal.

M Zecca1, S Micera, M C Carrozza, P Dario.   

Abstract

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contray, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none ofthese methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.

Entities:  

Mesh:

Year:  2002        PMID: 12739757     DOI: 10.1615/critrevbiomedeng.v30.i456.80

Source DB:  PubMed          Journal:  Crit Rev Biomed Eng        ISSN: 0278-940X


  67 in total

1.  Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-11-29       Impact factor: 4.538

2.  Multigrasp myoelectric control for a transradial prosthesis.

Authors:  Skyler A Dalley; Huseyin Atakan Varol; Michael Goldfarb
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

3.  Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography.

Authors:  Wonkeun Youn; Jung Kim
Journal:  Med Biol Eng Comput       Date:  2010-06-04       Impact factor: 2.602

4.  Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms.

Authors:  Jonathon W Sensinger; Blair A Lock; Todd A Kuiken
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-06-02       Impact factor: 3.802

5.  The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift.

Authors:  Aaron J Young; Levi J Hargrove; Todd A Kuiken
Journal:  IEEE Trans Biomed Eng       Date:  2011-06-09       Impact factor: 4.538

6.  A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury.

Authors:  Jie Liu; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-09-27       Impact factor: 3.802

7.  Proportional myoelectric control of a virtual object to investigate human efferent control.

Authors:  Keith E Gordon; Daniel P Ferris
Journal:  Exp Brain Res       Date:  2004-07-16       Impact factor: 1.972

8.  Design of a cybernetic hand for perception and action.

Authors:  M C Carrozza; G Cappiello; S Micera; B B Edin; L Beccai; C Cipriani
Journal:  Biol Cybern       Date:  2006-12-06       Impact factor: 2.086

9.  Study of stability of time-domain features for electromyographic pattern recognition.

Authors:  Dennis Tkach; He Huang; Todd A Kuiken
Journal:  J Neuroeng Rehabil       Date:  2010-05-21       Impact factor: 4.262

10.  Multi-subject/daily-life activity EMG-based control of mechanical hands.

Authors:  Claudio Castellini; Angelo Emanuele Fiorilla; Giulio Sandini
Journal:  J Neuroeng Rehabil       Date:  2009-11-17       Impact factor: 4.262

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