2013
DOI: 10.4236/jsip.2013.42013
|View full text |Cite
|
Sign up to set email alerts
|

Online Finger Gesture Recognition Using Surface Electromyography Signals

Abstract: The analysis on the online finger gesture recognition using multi-channel sEMG signals was explored in this paper. Nine types of gestures were applied to be identified, involving six kinds of numerical finger gestures and three kinds of hand gestures. The time domain parameters were extracted to be the features. And then, the probabilistic neural network was utilized to classify the proposed gestures with the extracted features. The experimental results showed that most of gestures could acquire the acceptable… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(1 citation statement)
references
References 13 publications
0
1
0
Order By: Relevance
“… Electromyography: ( a ) illustration of typical mid-arm EMG measurement setup for four independent sEMG channels [ 46 ]. Adapted from [ 46 ], licensed under CC BY 4.0; ( b ) intramuscular electromyography with muscle-implanted wireless electrode modules (Implantable Myoelectric Sensors). This figure was adapted from [ 45 ] with permission from Elsevier.…”
Section: Figurementioning
confidence: 99%
“… Electromyography: ( a ) illustration of typical mid-arm EMG measurement setup for four independent sEMG channels [ 46 ]. Adapted from [ 46 ], licensed under CC BY 4.0; ( b ) intramuscular electromyography with muscle-implanted wireless electrode modules (Implantable Myoelectric Sensors). This figure was adapted from [ 45 ] with permission from Elsevier.…”
Section: Figurementioning
confidence: 99%