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Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the hori...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875261/ https://www.ncbi.nlm.nih.gov/pubmed/35214585 http://dx.doi.org/10.3390/s22041686 |
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author | Nolazco-Flores, Juan Arturo Faundez-Zanuy, Marcos Velázquez-Flores, Oliver Alejandro Del-Valle-Soto, Carolina Cordasco, Gennaro Esposito, Anna |
author_facet | Nolazco-Flores, Juan Arturo Faundez-Zanuy, Marcos Velázquez-Flores, Oliver Alejandro Del-Valle-Soto, Carolina Cordasco, Gennaro Esposito, Anna |
author_sort | Nolazco-Flores, Juan Arturo |
collection | PubMed |
description | In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively. |
format | Online Article Text |
id | pubmed-8875261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88752612022-02-26 Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning Nolazco-Flores, Juan Arturo Faundez-Zanuy, Marcos Velázquez-Flores, Oliver Alejandro Del-Valle-Soto, Carolina Cordasco, Gennaro Esposito, Anna Sensors (Basel) Article In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively. MDPI 2022-02-21 /pmc/articles/PMC8875261/ /pubmed/35214585 http://dx.doi.org/10.3390/s22041686 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nolazco-Flores, Juan Arturo Faundez-Zanuy, Marcos Velázquez-Flores, Oliver Alejandro Del-Valle-Soto, Carolina Cordasco, Gennaro Esposito, Anna Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title | Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title_full | Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title_fullStr | Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title_full_unstemmed | Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title_short | Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning |
title_sort | mood state detection in handwritten tasks using pca–mfcbf and automated machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875261/ https://www.ncbi.nlm.nih.gov/pubmed/35214585 http://dx.doi.org/10.3390/s22041686 |
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