@article{
 title = {Intelligent system for identification of wheelchair user's posture using machine learning techniques},
 type = {article},
 year = {2019},
 keywords = {Embedded system,K-nearest neighbors,Kennard-stone,posture detection,principal component analysis},
 websites = {https://ieeexplore.ieee.org/document/8565996},
 id = {ad6726f2-4cd9-328b-8b88-a644ce0b636c},
 created = {2022-01-26T03:00:55.245Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:00:55.245Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Rosero-Montalvo2019c},
 private_publication = {false},
 abstract = {This paper presents an intelligent system aimed at detecting a person's posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: Selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard-Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.},
 bibtype = {article},
 author = {Rosero-Montalvo, Paul D. and Peluffo-Ordonez, Diego Hernn and Lopez Batista, Vivian Felix and Serrano, Jorge and Rosero, Edwin A.},
 doi = {10.1109/JSEN.2018.2885323},
 journal = {IEEE Sensors Journal}
}

{"_id":"t2pDZtixJisHS5puG","bibbaseid":"roseromontalvo-peluffoordonez-lopezbatista-serrano-rosero-intelligentsystemforidentificationofwheelchairuserspostureusingmachinelearningtechniques-2019","authorIDs":[],"author_short":["Rosero-Montalvo, P., D.","Peluffo-Ordonez, D., H.","Lopez Batista, V., F.","Serrano, J.","Rosero, E., A."],"bibdata":{"title":"Intelligent system for identification of wheelchair user's posture using machine learning techniques","type":"article","year":"2019","keywords":"Embedded system,K-nearest neighbors,Kennard-stone,posture detection,principal component analysis","websites":"https://ieeexplore.ieee.org/document/8565996","id":"ad6726f2-4cd9-328b-8b88-a644ce0b636c","created":"2022-01-26T03:00:55.245Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","group_id":"b9022d50-068c-31b4-9174-ebfaaf9ee57b","last_modified":"2022-01-26T03:00:55.245Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Rosero-Montalvo2019c","private_publication":false,"abstract":"This paper presents an intelligent system aimed at detecting a person's posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: Selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard-Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.","bibtype":"article","author":"Rosero-Montalvo, Paul D. and Peluffo-Ordonez, Diego Hernn and Lopez Batista, Vivian Felix and Serrano, Jorge and Rosero, Edwin A.","doi":"10.1109/JSEN.2018.2885323","journal":"IEEE Sensors Journal","bibtex":"@article{\n title = {Intelligent system for identification of wheelchair user's posture using machine learning techniques},\n type = {article},\n year = {2019},\n keywords = {Embedded system,K-nearest neighbors,Kennard-stone,posture detection,principal component analysis},\n websites = {https://ieeexplore.ieee.org/document/8565996},\n id = {ad6726f2-4cd9-328b-8b88-a644ce0b636c},\n created = {2022-01-26T03:00:55.245Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:00:55.245Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Rosero-Montalvo2019c},\n private_publication = {false},\n abstract = {This paper presents an intelligent system aimed at detecting a person's posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: Selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard-Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.},\n bibtype = {article},\n author = {Rosero-Montalvo, Paul D. and Peluffo-Ordonez, Diego Hernn and Lopez Batista, Vivian Felix and Serrano, Jorge and Rosero, Edwin A.},\n doi = {10.1109/JSEN.2018.2885323},\n journal = {IEEE Sensors Journal}\n}","author_short":["Rosero-Montalvo, P., D.","Peluffo-Ordonez, D., H.","Lopez Batista, V., F.","Serrano, J.","Rosero, E., A."],"urls":{"Website":"https://ieeexplore.ieee.org/document/8565996"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"roseromontalvo-peluffoordonez-lopezbatista-serrano-rosero-intelligentsystemforidentificationofwheelchairuserspostureusingmachinelearningtechniques-2019","role":"author","keyword":["Embedded system","K-nearest neighbors","Kennard-stone","posture detection","principal component analysis"],"metadata":{"authorlinks":{}},"downloads":9},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","creationDate":"2020-02-26T04:21:14.444Z","downloads":9,"keywords":["embedded system","k-nearest neighbors","kennard-stone","posture detection","principal component analysis"],"search_terms":["intelligent","system","identification","wheelchair","user","posture","using","machine","learning","techniques","rosero-montalvo","peluffo-ordonez","lopez batista","serrano","rosero"],"title":"Intelligent system for identification of wheelchair user's posture using machine learning techniques","year":2019,"dataSources":["YEF3uFAbDNQXrkgNw","ya2CyA73rpZseyrZ8","ntXyXv2964fDt3myF"]}