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Mood state detection in handwritten tasks using PCA–mFCBF and automated machine learning
(MDPI, 2022 Feb 2)
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 ...
Emotional state recognition performance improvement on a handwriting and drawing task
(IEEE, 2021-02-21)
In this work we combine time, spectral and cepstral features of the signal captured in a tablet to characterize depression, anxiety, and stress emotional state recognition on the EMOTHAW database. EMOTHAW ...
Comparison of CNN-learned vs. handcrafted features for detection of Parkinson's disease dysgraphia in a multilingual dataset
(Frontiers Media S.A., 2022)
Parkinson’s disease dysgraphia (PDYS), one of the earliest signs of Parkinson’s disease
(PD), has been researched as a promising biomarker of PD and as the target of
a noninvasive and inexpensive ...
Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
(IEEE, 2021-10-22)
Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized ...