Hand gesture recognition based on surface electromyography. Samadani, A. & Kulić, D. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, pages 4196--9, 2014. abstract bibtex 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.
@InProceedings{Samadani2014,
Title = {Hand gesture recognition based on surface electromyography},
Author = {Samadani, A. and Kuli\'{c}, D.},
Booktitle = {Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society},
Year = {2014},
Pages = {4196--9},
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.},
Owner = {jf2lin},
Timestamp = {2015.05.21}
}
Downloads: 0
{"_id":"t58PfYJiXpkvFnxQT","bibbaseid":"samadani-kuli-handgesturerecognitionbasedonsurfaceelectromyography-2014","downloads":0,"creationDate":"2017-09-14T16:34:37.022Z","title":"Hand gesture recognition based on surface electromyography","author_short":["Samadani, A.","Kulić, D."],"year":2014,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/jfslin/jfslin.github.io/master/jf2lin.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Hand gesture recognition based on surface electromyography","author":[{"propositions":[],"lastnames":["Samadani"],"firstnames":["A."],"suffixes":[]},{"propositions":[],"lastnames":["Kulić"],"firstnames":["D."],"suffixes":[]}],"booktitle":"Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society","year":"2014","pages":"4196--9","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.","owner":"jf2lin","timestamp":"2015.05.21","bibtex":"@InProceedings{Samadani2014,\n Title = {Hand gesture recognition based on surface electromyography},\n Author = {Samadani, A. and Kuli\\'{c}, D.},\n Booktitle = {Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society},\n Year = {2014},\n Pages = {4196--9},\n\n 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.},\n Owner = {jf2lin},\n Timestamp = {2015.05.21}\n}\n\n","author_short":["Samadani, A.","Kulić, D."],"key":"Samadani2014","id":"Samadani2014","bibbaseid":"samadani-kuli-handgesturerecognitionbasedonsurfaceelectromyography-2014","role":"author","urls":{},"downloads":0},"search_terms":["hand","gesture","recognition","based","surface","electromyography","samadani","kulić"],"keywords":[],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}