Cangelosi, AntonioAntola, GabrieleIacono, Alberto LoSantamaria, AlfonsoClerico, MarinellaAl-Thani, DenaAgus, MarcoCalì, CorradoBanterle, FrancescoCaggianese, GiuseppeCapece, NicolaErra, UgoLupinetti, KatiaManfredi, Gilda2023-11-122023-11-122023978-3-03868-235-62617-4855https://doi.org/10.2312/stag.20231306https://diglib.eg.org:443/handle/10.2312/stag20231306Accurate and early diagnosis of neuropsychiatric disorders, such as Autism Spectrum Disorders (ASD) is a significant challenge in clinical practice. This study explores the use of real-time gaze tracking as a tool for unbiased and quantitative analysis of eye gaze. The results of this study could support the diagnosis of disorders and potentially be used as a tool in the field of rehabilitation. The proposed setup consists of an RGB-D camera embedded in the latest-generation smartphones and a set of processing components for the analysis of recorded data related to patient interactivity. The proposed system is easy to use and doesn't require much knowledge or expertise. It also achieves a high level of accuracy. Because of this, it can be used remotely (telemedicine) to simplify diagnosis and rehabilitation processes. We present initial findings that show how real-time gaze tracking can be a valuable tool for doctors. It is a non-invasive device that provides unbiased quantitative data that can aid in early detection, monitoring, and treatment evaluation. This study's findings have significant implications for the advancement of ASD research. The innovative approach proposed in this study has the potential to enhance diagnostic accuracy and improve patient outcomes.Attribution 4.0 International LicenseCCS Concepts: Applied computing -> Health informatics; Human-centered computing -> Pointing devicesApplied computingHealth informaticsHuman centered computingPointing devices10.2312/stag.20231306161-1633 pages