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Selection of suitable hand gestures for reliable myoelectric human computer interface

Overview of attention for article published in BioMedical Engineering OnLine, April 2015
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1 tweeter

Citations

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23 Dimensions

Readers on

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68 Mendeley
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Title
Selection of suitable hand gestures for reliable myoelectric human computer interface
Published in
BioMedical Engineering OnLine, April 2015
DOI 10.1186/s12938-015-0025-5
Pubmed ID
Authors

Maria Claudia F Castro, Sridhar P Arjunan, Dinesh K Kumar

Abstract

Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity. Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices. When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion. This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Korea, Republic of 1 1%
Mexico 1 1%
Unknown 64 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 19%
Student > Bachelor 10 15%
Student > Master 10 15%
Researcher 8 12%
Student > Doctoral Student 5 7%
Other 9 13%
Unknown 13 19%
Readers by discipline Count As %
Engineering 30 44%
Computer Science 9 13%
Medicine and Dentistry 4 6%
Nursing and Health Professions 2 3%
Sports and Recreations 2 3%
Other 4 6%
Unknown 17 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2015.
All research outputs
#4,182,894
of 5,003,000 outputs
Outputs from BioMedical Engineering OnLine
#277
of 340 outputs
Outputs of similar age
#121,405
of 144,400 outputs
Outputs of similar age from BioMedical Engineering OnLine
#17
of 18 outputs
Altmetric has tracked 5,003,000 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 340 research outputs from this source. They receive a mean Attention Score of 1.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.