Robust remote detection of depressive tendency based on keystroke dynamics and behavioural characteristics

• Published in: Scientific reports Vol. 14; no. 1; pp. 28025 - 13
• Main Authors: Fadul, Ruba, AlShehhi, Aamna, Hadjileontiadis, Leontios
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1386; 692/53/2421; 692/699/476/1414; Depression disease; Detection; Humanities and Social Sciences; Interactive computer systems; Keystroke dynamics; Machine learning; Mental depression; Mental disorders; multidisciplinary; Remote screening; Science; Science (multidisciplinary); Smartphones; Typing; Remote screening; Typing; Detection; Depression disease; Keystroke dynamics; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-78489-x

Depressive Disorder (DD) is a leading cause of disability worldwide. Screening tools for detecting DD symptoms are essential for monitoring and efficient managing. Remarkably, individuals’ kinetic activities, including their interaction with touchscreen smartphones, can be a proxy for their mental status. Therefore, studying these typing patterns can assist in developing passive screening tools for detecting even the early stage of DD, i.e., the depressive tendency (DT). Here we extend a previous study by exploring different machine learning models with various feature engineering approaches to detect the subjects’ DT, as indicated by the self-administered Patient Health Questionnaire-9 (PHQ-9) score, via keystroke digital biomarkers. The keystroke timing sequences were unobtrusively collected from 24 subjects during routine interaction with touchscreen smartphones, resulting in 23,264 typing sessions. The proposed framework was investigated under two keystroke feature combinations—hold-time and flight-time variables—and validated using nested cross-validation scheme. Different feature selection (FS) techniques were employed to select informative features from the keystroke sequences. The best-performing gradient boosting classifier with features selected by the mutual information FS method achieved an improved Area Under Curve (AUC) of 0.98 [95% confidence interval: 0.91-1.00]. The proposed DT pipeline, which surpasses the state-of-the-art models, could effectively capture DT, considering users’ behavioural characteristics. This would potentially provide users with information regarding the evolution of their mental health, simultaneously contributing to improving digital tools for objectively screening mental disorders in-the-wild.