Human-sitting-pose detection using data classification and dimensionality reduction. Nunez-Godoy, S., Alvear-Puertas, V., Realpe-Godoy, S., Pujota-Cuascota, E., Farinango-Endara, H., Navarrete-Insuasti, I., Vaca-Chapi, F., Rosero-Montalvo, P., & Peluffo, D., H. In 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016, 2016. doi abstract bibtex The research area of sitting-pose analysis allows for preventing a range of physical health problems mainly physical. Despite that different systems have been proposed for sitting-pose detection, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an alternative approach based on a sensor network to acquire the position-related variables and machine learning techniques, namely dimensionality reduction (DR) and classification. Since the information acquired by sensors is high-dimensional and therefore it might not be saved into embedded system memory, a DR stage based on principal component analysis (PCA) is performed. Subsequently, the automatic posed detection is carried out by the k-nearest neighbors (KNN) classifier. As a result, regarding using the whole data set, the computational cost is decreased by 33 % as well as the data reading is reduced by 10 ms. Then, sitting-pose detection task takes 26 ms, and reaches 75% of accuracy in a 4-trial experiment.
@inproceedings{
title = {Human-sitting-pose detection using data classification and dimensionality reduction},
type = {inproceedings},
year = {2016},
keywords = {PCA,chair position,embedded system,knn},
id = {3ecc469a-e5fb-3089-a1b3-13c5725efe84},
created = {2018-04-17T02:36:14.238Z},
file_attached = {false},
profile_id = {f01ceea9-1014-347a-b89d-aa69782ea2ee},
last_modified = {2021-10-05T13:36:21.170Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {false},
hidden = {false},
private_publication = {true},
abstract = {The research area of sitting-pose analysis allows for preventing a range of physical health problems mainly physical. Despite that different systems have been proposed for sitting-pose detection, some open issues are still to be dealt with, such as: Cost, computational load, accuracy, portability, and among others. In this work, we present an alternative approach based on a sensor network to acquire the position-related variables and machine learning techniques, namely dimensionality reduction (DR) and classification. Since the information acquired by sensors is high-dimensional and therefore it might not be saved into embedded system memory, a DR stage based on principal component analysis (PCA) is performed. Subsequently, the automatic posed detection is carried out by the k-nearest neighbors (KNN) classifier. As a result, regarding using the whole data set, the computational cost is decreased by 33 % as well as the data reading is reduced by 10 ms. Then, sitting-pose detection task takes 26 ms, and reaches 75% of accuracy in a 4-trial experiment.},
bibtype = {inproceedings},
author = {Nunez-Godoy, Santiago and Alvear-Puertas, Vanessa and Realpe-Godoy, Staling and Pujota-Cuascota, Edwin and Farinango-Endara, Henry and Navarrete-Insuasti, Ivan and Vaca-Chapi, Franklin and Rosero-Montalvo, Paul and Peluffo, Diego H.},
doi = {10.1109/ETCM.2016.7750822},
booktitle = {2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016}
}
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