Now showing items 1-4 of 4
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
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
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 ...