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.

Handwriting and drawing features for detecting negative moods

Cordasco, Gennaro;Esposito, Anna
2019

Abstract

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.
Cordasco, Gennaro; Scibelli, Filomena; Faundez-Zanuy, Marcos; Likforman-Sulem, Laurence; Esposito, Anna
File in questo prodotto:
File Dimensione Formato  
CordascoWirn2017.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 645.49 kB
Formato Adobe PDF
645.49 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/400851
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact