Discriminative Power of Handwriting and Drawing Features in Depression

Author:

Greco Claudia1ORCID,Raimo Gennaro1ORCID,Amorese Terry1ORCID,Cuciniello Marialucia1ORCID,Mcconvey Gavin2,Cordasco Gennaro1ORCID,Faundez-Zanuy Marcos3ORCID,Vinciarelli Alessandro4ORCID,Callejas-Carrion Zoraida5ORCID,Esposito Anna1ORCID

Affiliation:

1. Department of Psychology, Università della Campania “Luigi Vanvitelli”, Viale Ellittico 31 Caserta, 81000, Italy

2. Action Mental Health, 27 Jubilee Rd, BT23 4YH, Newtownards, UK

3. Tecnocampus Universitat Pompeu Fabra, Carrer d’Ernest Lluch 32 Mataro, Barcelona 08302, Spain

4. University of Glasgow, School of Computing Science, 18 Lilybank Gardens Glasgow, G12,8RZ, Scotland

5. Department of Languages and Computer Systems, Universidad de Granada, Periodista Daniel Saucedo Aranda Granada, 18071, Spain

Abstract

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features’ categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.

Funder

the European Union Horizon 2020 research and innovation program

the project SIROBOTICS that received funding from Ministero dell’Istruzione, dell’Università, e della Ricerca

the project ANDROIDS that received funding from Università della Campania “Luigi Vanvitelli” inside the program V:ALERE 2019

the project SALICE that received funding from Università della Campania “Luigi Vanvitelli” inside the program Giovani Ricercatori

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

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