Literature DB >> 22275201

Control of hand prostheses using peripheral information.

Silvestro Micera1, Jacopo Carpaneto, Stanisa Raspopovic.   

Abstract

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.

Mesh:

Year:  2010        PMID: 22275201     DOI: 10.1109/RBME.2010.2085429

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  41 in total

1.  Object stiffness recognition using haptic feedback delivered through transcutaneous proximal nerve stimulation.

Authors:  Luis Vargas; Henry Shin; He Helen Huang; Yong Zhu; Xiaogang Hu
Journal:  J Neural Eng       Date:  2019-12-05       Impact factor: 5.379

2.  Dexterous control of a prosthetic hand using fine-wire intramuscular electrodes in targeted extrinsic muscles.

Authors:  Christian Cipriani; Jacob L Segil; J Alex Birdwell; Richard F ff Weir
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2014-01-21       Impact factor: 3.802

3.  Ranking hand movements for myoelectric pattern recognition considering forearm muscle structure.

Authors:  Youngjin Na; Sangjoon J Kim; Sungho Jo; Jung Kim
Journal:  Med Biol Eng Comput       Date:  2017-01-04       Impact factor: 2.602

4.  Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol.

Authors:  Cassie N Borish; Adam Feinman; Matteo Bertucco; Natalie G Ramsy; Terence D Sanger
Journal:  J Neurophysiol       Date:  2018-01-31       Impact factor: 2.714

5.  Real-time simulation of hand motion for prosthesis control.

Authors:  Dimitra Blana; Edward K Chadwick; Antonie J van den Bogert; Wendy M Murray
Journal:  Comput Methods Biomech Biomed Engin       Date:  2016-11-20       Impact factor: 1.763

6.  Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

Authors:  Silvestro Micera; Paolo M Rossini; Jacopo Rigosa; Luca Citi; Jacopo Carpaneto; Stanisa Raspopovic; Mario Tombini; Christian Cipriani; Giovanni Assenza; Maria C Carrozza; Klaus-Peter Hoffmann; Ken Yoshida; Xavier Navarro; Paolo Dario
Journal:  J Neuroeng Rehabil       Date:  2011-09-05       Impact factor: 4.262

7.  Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders.

Authors:  Shobha Jose; S Thomas George; M S P Subathra; Vikram Shenoy Handiru; Poornaselvan Kittu Jeevanandam; Umberto Amato; Easter Selvan Suviseshamuthu
Journal:  IEEE Open J Eng Med Biol       Date:  2020-08-17

8.  Virtual reality environment for simulating tasks with a myoelectric prosthesis: an assessment and training tool.

Authors:  Joris M Lambrecht; Christopher L Pulliam; Robert F Kirsch
Journal:  J Prosthet Orthot       Date:  2011-04

Review 9.  On the viability of implantable electrodes for the natural control of artificial limbs: review and discussion.

Authors:  Max Ortiz-Catalan; Rickard Brånemark; Bo Håkansson; Jean Delbeke
Journal:  Biomed Eng Online       Date:  2012-06-20       Impact factor: 2.819

10.  Continuous decoding of grasping tasks for a prospective implantable cortical neuroprosthesis.

Authors:  Jacopo Carpaneto; Vassilis Raos; Maria A Umiltà; Leonardo Fogassi; Akira Murata; Vittorio Gallese; Silvestro Micera
Journal:  J Neuroeng Rehabil       Date:  2012-11-26       Impact factor: 4.262

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