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.
Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques [link]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},
 id = {0324272f-0ac5-3c16-ba5e-a20f9b825330},
 created = {2022-01-26T03:01:03.817Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:01:03.817Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 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)}
}

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