2026-04-13T06:50:13Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12367/21952026-02-18T08:59:12Zcom_2072_484362col_2072_484363

00925njm 22002777a 4500 dc Nolazco Flores, Juan Arturo author Faundez-Zanuy, Marcos author Velázquez-Flores, O. A. author Cordasco, Gennaro author Esposito, Anna author 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 contains the emotional states of users represented by capturing signals from sensors on the tablet and pen when the user is performing 3 specific handwriting and 4 drawing tasks, which had been categorized into depressed, anxious, stressed, and typical, according to the Depression, Anxiety and Stress Scale (DASS). Each user was characterized with six time-domain features, and the number of spectral-domain and cepstral-domain features for the horizontal and vertical displacement of the pen, the pressure on the paper, and the time spent on-air and off-air, depended on the configuration of the filterbank [...]. Nolazco Flores JA, Faundez-Zanuy M, Velázquez-Flores OA, Cordasco G, Esposito A. Emotional state recognition performance improvement on a handwriting and drawing task. IEEE Access. 2021 Feb 23;(9):28496-28504. DOI: 10.1109/ACCESS.2021.3058443 2169-3536 https://hdl.handle.net/20.500.12367/2195 10.1109/ACCESS.2021.3058443 Data augmentation Emotional state recognition Emotional states Feature extraction SVM Emotional state recognition performance improvement on a handwriting and drawing task