@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)}
}

{"_id":"6stFDTW4JaMkufc84","bibbaseid":"roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018","authorIDs":[],"author_short":["Rosero-Montalvo, P., D.","Godoy-Trujillo, P.","Flores-Bosmediano, E.","Carrascal-Garcia, J.","Otero-Potosi, S.","Benitez-Pereira, H.","Peluffo-Ordonez, D., H."],"bibdata":{"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)","bibtex":"@inproceedings{\n title = {Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques},\n type = {inproceedings},\n year = {2018},\n keywords = {intelligent glove,knn,prototype selection,sign language},\n pages = {1-5},\n websites = {https://ieeexplore.ieee.org/document/8580268/},\n month = {10},\n publisher = {IEEE},\n id = {0324272f-0ac5-3c16-ba5e-a20f9b825330},\n created = {2022-01-26T03:01:03.817Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:01:03.817Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Rosero-Montalvo2018a},\n private_publication = {false},\n 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.},\n bibtype = {inproceedings},\n 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.},\n doi = {10.1109/ETCM.2018.8580268},\n booktitle = {2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM)}\n}","author_short":["Rosero-Montalvo, P., D.","Godoy-Trujillo, P.","Flores-Bosmediano, E.","Carrascal-Garcia, J.","Otero-Potosi, S.","Benitez-Pereira, H.","Peluffo-Ordonez, D., H."],"urls":{"Website":"https://ieeexplore.ieee.org/document/8580268/"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"roseromontalvo-godoytrujillo-floresbosmediano-carrascalgarcia-oteropotosi-benitezpereira-peluffoordonez-signlanguagerecognitionbasedonintelligentgloveusingmachinelearningtechniques-2018","role":"author","keyword":["intelligent glove","knn","prototype selection","sign language"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"inproceedings","creationDate":"2020-12-01T23:20:09.355Z","downloads":1,"keywords":["intelligent glove","knn","prototype selection","sign language"],"search_terms":["sign","language","recognition","based","intelligent","glove","using","machine","learning","techniques","rosero-montalvo","godoy-trujillo","flores-bosmediano","carrascal-garcia","otero-potosi","benitez-pereira","peluffo-ordonez"],"title":"Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques","year":2018,"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","dataSources":["YEF3uFAbDNQXrkgNw","ya2CyA73rpZseyrZ8","ntXyXv2964fDt3myF","2252seNhipfTmjEBQ"]}