Literature DB >> 10149040

Practical methods for controlling powered upper-extremity prostheses.

T W Williams1.   

Abstract

Myoelectric pickups (electrodes and processors for detecting the signal that is recorded as an electromyogram) are the most important human-machine interface for controlling powered upper-extremity prostheses. This article presents a simple explanation of myoelectric signal acquisition and then discusses how these signals are used to control the small motors in electric hands, elbows, wrist rotators, and other similar equipment. The less-familiar switch-based and proportional position-sensing controls are also explained. A complete listing of the major suppliers and products available will aid in understanding a discussion of the criteria for using external power instead of, or along with, body power to control and activate prosthetic function.

Entities:  

Mesh:

Year:  1990        PMID: 10149040     DOI: 10.1080/10400435.1990.10132142

Source DB:  PubMed          Journal:  Assist Technol        ISSN: 1040-0435


  15 in total

1.  Comparison of electromyography and force as interfaces for prosthetic control.

Authors:  Elaine A Corbett; Eric J Perreault; Todd A Kuiken
Journal:  J Rehabil Res Dev       Date:  2011

2.  An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface.

Authors:  He Huang; Ping Zhou; Guanglin Li; Todd A Kuiken
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-02       Impact factor: 3.802

3.  Activation of individual extrinsic thumb muscles and compartments of extrinsic finger muscles.

Authors:  J Alexander Birdwell; Levi J Hargrove; Todd A Kuiken; Richard F Ff Weir
Journal:  J Neurophysiol       Date:  2013-06-26       Impact factor: 2.714

4.  Design of a robust EMG sensing interface for pattern classification.

Authors:  He Huang; Fan Zhang; Yan L Sun; Haibo He
Journal:  J Neural Eng       Date:  2010-09-01       Impact factor: 5.379

5.  Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.

Authors:  He Huang; Fan Zhang; Levi J Hargrove; Zhi Dou; Daniel R Rogers; Kevin B Englehart
Journal:  IEEE Trans Biomed Eng       Date:  2011-07-14       Impact factor: 4.538

6.  Real-time implementation of an intent recognition system for artificial legs.

Authors:  Fan Zhang; Zhi Dou; Michael Nunnery; He Huang
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

7.  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

8.  A strategy for identifying locomotion modes using surface electromyography.

Authors:  He Huang; Todd A Kuiken; Robert D Lipschutz
Journal:  IEEE Trans Biomed Eng       Date:  2009-01       Impact factor: 4.538

9.  Spatial filtering improves EMG classification accuracy following targeted muscle reinnervation.

Authors:  He Huang; Ping Zhou; Guanglin Li; Todd Kuiken
Journal:  Ann Biomed Eng       Date:  2009-06-13       Impact factor: 3.934

10.  EMG-Force and EMG-Target Models During Force-Varying Bilateral Hand-Wrist Contraction in Able-Bodied and Limb-Absent Subjects.

Authors:  Ziling Zhu; Carlos Martinez-Luna; Jianan Li; Benjamin E McDonald; Chenyun Dai; Xinming Huang; Todd R Farrell; Edward A Clancy
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2021-01-28       Impact factor: 3.802

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