@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 = {e77821ff-b41a-304c-8ccb-cc74a3c124ad},
 created = {2022-02-02T07:25:42.987Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {55483c42-1c6e-3138-8b55-8d557318ba00},
 last_modified = {2022-02-02T07:25:42.987Z},
 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}
}

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