Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques. Rosero-Montalvo, P., D., Godoy-Trujillo, P., Flores-Bosmediano, E., Carrascal-Garcia, J., Otero-Potosi, S., Benitez-Pereira, H., & Peluffo-Ordonez, D., H. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), pages 1-5, 10, 2018. IEEE. Website doi abstract bibtex 1 download We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.
@inproceedings{
title = {Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques},
type = {inproceedings},
year = {2018},
keywords = {intelligent glove,knn,prototype selection,sign language},
pages = {1-5},
websites = {https://ieeexplore.ieee.org/document/8580268/},
month = {10},
publisher = {IEEE},
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created = {2022-01-26T03:01:03.817Z},
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confirmed = {true},
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citation_key = {Rosero-Montalvo2018a},
private_publication = {false},
abstract = {We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.},
bibtype = {inproceedings},
author = {Rosero-Montalvo, Paul D. and Godoy-Trujillo, Pamela and Flores-Bosmediano, Edison and Carrascal-Garcia, Jorge and Otero-Potosi, Santiago and Benitez-Pereira, Henry and Peluffo-Ordonez, Diego H.},
doi = {10.1109/ETCM.2018.8580268},
booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}
}
Downloads: 1
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