{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:41:27Z","timestamp":1766158887502,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation\u2013based 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.<\/jats:p>","DOI":"10.3390\/s22041686","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:48:41Z","timestamp":1645476521000},"page":"1686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mood State Detection in Handwritten Tasks Using PCA\u2013mFCBF and Automated Machine Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4187-9352","authenticated-orcid":false,"given":"Juan Arturo","family":"Nolazco-Flores","sequence":"first","affiliation":[{"name":"School of Engineering and Science, Tecnol\u00f3gico de Monterrey, Monterrey 64849, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0605-1282","authenticated-orcid":false,"given":"Marcos","family":"Faundez-Zanuy","sequence":"additional","affiliation":[{"name":"Escola Superior Politecnica, TecnoCampus Mataro-Maresme, 08302 Mataro, Spain"}]},{"given":"Oliver Alejandro","family":"Vel\u00e1zquez-Flores","sequence":"additional","affiliation":[{"name":"School of Engineering and Science, Tecnol\u00f3gico de Monterrey, Monterrey 64849, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0272-3275","authenticated-orcid":false,"given":"Carolina","family":"Del-Valle-Soto","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, Zapopan 45010, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9148-9769","authenticated-orcid":false,"given":"Gennaro","family":"Cordasco","sequence":"additional","affiliation":[{"name":"Dipartimento di Psicologia, Universit\u00e0 della Campania \u2018Luigi Vanvitelli\u2019 and IIASS, 81100 Caserta, Italy"}]},{"given":"Anna","family":"Esposito","sequence":"additional","affiliation":[{"name":"Dipartimento di Psicologia, Universit\u00e0 della Campania \u2018Luigi Vanvitelli\u2019 and IIASS, 81100 Caserta, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/34.824821","article-title":"Online and off-line handwriting recognition: A comprehensive survey","volume":"22","author":"Plamondon","year":"2000","journal-title":"IEEE Trans. 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