@inproceedings{
 title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},
 type = {inproceedings},
 year = {2021},
 pages = {55-67},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {4317f039-bd45-365f-9dc1-03ce2b3f1e14},
 created = {2021-10-23T00:26:10.704Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b8c873a2-ed58-396e-b124-8a273665874f},
 last_modified = {2021-10-23T00:26:10.704Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-030-89654-6_5},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\% rating yield using the proposed methodology with the Fast Tree algorithm.},
 bibtype = {inproceedings},
 author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},
 editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},
 booktitle = {Applied Informatics}
}

{"_id":"XDu7nQXrkpY4XDGxD","bibbaseid":"lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021","author_short":["López-Albán, D.","López-Barrera, A.","Mayorca-Torres, D.","Peluffo-Ordóñez, D."],"bibdata":{"title":"Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques","type":"inproceedings","year":"2021","pages":"55-67","websites":"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5","publisher":"Springer International Publishing","city":"Cham","id":"4317f039-bd45-365f-9dc1-03ce2b3f1e14","created":"2021-10-23T00:26:10.704Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","group_id":"b8c873a2-ed58-396e-b124-8a273665874f","last_modified":"2021-10-23T00:26:10.704Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"10.1007/978-3-030-89654-6_5","source_type":"inproceedings","private_publication":false,"abstract":"The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\% rating yield using the proposed methodology with the Fast Tree algorithm.","bibtype":"inproceedings","author":"López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D","editor":"Florez, Hector and Pollo-Cattaneo, Ma Florencia","booktitle":"Applied Informatics","bibtex":"@inproceedings{\n title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},\n type = {inproceedings},\n year = {2021},\n pages = {55-67},\n websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {4317f039-bd45-365f-9dc1-03ce2b3f1e14},\n created = {2021-10-23T00:26:10.704Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b8c873a2-ed58-396e-b124-8a273665874f},\n last_modified = {2021-10-23T00:26:10.704Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-030-89654-6_5},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\\% rating yield using the proposed methodology with the Fast Tree algorithm.},\n bibtype = {inproceedings},\n author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},\n editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},\n booktitle = {Applied Informatics}\n}","author_short":["López-Albán, D.","López-Barrera, A.","Mayorca-Torres, D.","Peluffo-Ordóñez, D."],"editor_short":["Florez, H.","Pollo-Cattaneo, M., F."],"urls":{"Website":"https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"lpezalbn-lpezbarrera-mayorcatorres-peluffoordez-signlanguagerecognitionusingleapmotionbasedontimefrequencycharacterizationandconventionalmachinelearningtechniques-2021","role":"author","metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","dataSources":["bf4Wvyy9Na82zPs7u","ya2CyA73rpZseyrZ8"],"keywords":[],"search_terms":["sign","language","recognition","using","leap","motion","based","time","frequency","characterization","conventional","machine","learning","techniques","lópez-albán","lópez-barrera","mayorca-torres","peluffo-ordóñez"],"title":"Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques","year":2021}