Emotional State Recognition Performance Improvement on a Handwriting and Drawing Task

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 contains the emotional states of users represented by capturing signals from sensors on the tab...

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Bibliographic Details
Main Authors: Juan A. Nolazco-Flores, Marcos Faundez-Zanuy, Oliver A. Velazquez-Flores, Gennaro Cordasco, Anna Esposito
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Data augmentation
emotional state recognition
emotional states
feature extraction
SVM
Online Access:https://ieeexplore.ieee.org/document/9352470/

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