Intelligent system for identification of wheelchair user's posture using machine learning techniques. Rosero-Montalvo, P., D., Peluffo-Ordonez, D., H., Lopez Batista, V., F., Serrano, J., & Rosero, E., A. IEEE Sensors Journal, 2019.
Intelligent system for identification of wheelchair user's posture using machine learning techniques [link]Website  abstract   bibtex   
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.
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 title = {Intelligent system for identification of wheelchair user's posture using machine learning techniques},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 keywords = {Embedded system,K-nearest neighbors,Kennard-stone,posture detection,principal component analysis},
 websites = {https://ieeexplore.ieee.org/document/8565996},
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 created = {2021-05-10T23:31:46.885Z},
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 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.},
 journal = {IEEE Sensors Journal}
}

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