Grapho-Phonological Signals-Based Machine Learning Models Development for the Depressive Disorder

Mental disorders, encompassing conditions like depression, pose a significant global health challenge. Traditional diagnostic methods, while valuable, have inherent limitations, necessitating the exploration of innovative approaches. In this study, we delved into the analysis of grapho-phonological data as potential objective indicators of depression. Our research journey began with an analysis of an existing database, containing handwriting and drawing data from both healthy individuals and those diagnosed with depressive disorders. Leveraging this data, we developed machine learning models designed to distinguish between these two groups. Notably, we observed that models utilizing drawing-related features outperformed those relying on writing features. This section of our study highlights the potential of combining these data for enhanced depression detection. The creation of a new database is a noteworthy addition, providing grapho-phonological data (i.e., handwriting, drawing and laughter data) from healthy and depressed subjects. The analysis of these data sets reached peak performance, achieving superior accuracy values in comprehensive classification models, underscoring the potential of this multi-modal approaches. The study also presents a streamlined, non-invasive protocol that allows to efficiently gather essential grapho-phonological data, taking only approximately 7 min per participant. Moreover, our work introduces the “Voice & Drawing App”, an accessible tool enabling remote data collection for mental health assessment. This innovation aligns with telemonitoring trends and offers a user-friendly solution, whether within clinical settings or patients’ homes. An accelerometer-based inertial measurement units system (IMUs) to capture motion patterns has been introduced into the app. This choice is driven by the recognition of the valuable information encoded in psychomotor behaviour, that can serve as indicators of depression. Our study highlights promising avenues for future research, including expanding subject populations, implementing automatic laughter recognition, and incorporating video data to capture facial expressions. Wearable devices with IMUs offer exciting opportunities for comprehensive depression assessment. In summary, our research seeks to improve our understanding of depression by exploring innovative approaches that incorporate graphological signals, laughter, and IMUs. These multidimensional strategies aim to enhance the accuracy of depression detection and telemonitoring, ultimately facilitating more timely and effective interventions in mental health care.