Handwriting and Drawing Features for Detecting Negative Moods - Télécom Paris Accéder directement au contenu
Chapitre D'ouvrage Année : 2019

Handwriting and Drawing Features for Detecting Negative Moods

Résumé

In order to provide support to the implementation of on-line and remote systems for the early detection of interactional disorders, this paper reports on the exploitation of handwriting and drawing features for detecting negative moods. The features are collected from depressed, stressed, and anxious subjects, assessed with DASS-42, and matched by age and gender with handwriting and drawing features of typically ones. Mixed ANOVA analyses, based on a binary categorization of the groups, reveal significant differences among features collected from subjects with negative moods with respect to the control group depending on the involved exercises and features categories (in time or frequency of the considered events). In addition, the paper reports the description of a large database of handwriting and drawing features collected from 240 subjects
Fichier non déposé

Dates et versions

hal-04277476 , version 1 (09-11-2023)

Identifiants

Citer

Gennaro Cordasco, Filomena Scibelli, Marcos Faundez-Zanuy, Laurence Likforman-Sulem, Anna Esposito. Handwriting and Drawing Features for Detecting Negative Moods. Quantifying and Processing Biomedical and Behavioral Signals, 103, Springer International Publishing, pp.73-86, 2019, Smart Innovation, Systems and Technologies, ⟨10.1007/978-3-319-95095-2_7⟩. ⟨hal-04277476⟩
12 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More