DocumentCode :
140351
Title :
Hand gesture recognition based on surface electromyography
Author :
Samadani, Ali-Akbar ; Kulic, Dana
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Waterloo, Waterloo, ON, Canada
fYear :
2014
fDate :
26-30 Aug. 2014
Firstpage :
4196
Lastpage :
4199
Abstract :
Human hands are the most dexterous of human limbs and hand gestures play an important role in non-verbal communication. Underlying electromyograms associated with hand gestures provide a wealth of information based on which varying hand gestures can be recognized. This paper develops an inter-individual hand gesture recognition model based on Hidden Markov models that receives surface electromyography (sEMG) signals as inputs and predicts a corresponding hand gesture. The developed recognition model is tested with a dataset of 10 various hand gestures performed by 25 subjects in a leave-one-subject-out cross validation and an inter-individual recognition rate of 79% was achieved. The promising recognition rate demonstrates the efficacy of the proposed approach for discriminating between gesture-specific sEMG signals and could inform the design of sEMG-controlled prostheses and assistive devices.
Keywords :
electromyography; gesture recognition; hidden Markov models; medical signal processing; prosthetics; Hidden Markov models; assistive devices; electromyograms; gesture-specific sEMG signals; human hands; human limbs; interindividual hand gesture recognition model; interindividual recognition rate; leave-one-subject-out cross validation; nonverbal communication; sEMG-controlled prostheses; surface electromyography signals; Electromyography; Gesture recognition; Hidden Markov models; Indexes; Testing; Thumb; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2014.6944549
Filename :
6944549
Link To Document :
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