Core Insights - A breakthrough technology has been developed by the Institute of Software Research, Chinese Academy of Sciences, in collaboration with the First Hospital of Hebei Medical University, which efficiently identifies emotional disorders such as anxiety and depression through eye movement analysis during natural viewing of table tennis matches [1][2] - This technology offers a non-invasive and convenient new solution for mental health monitoring, with results published in the prestigious journal "Frontiers in Neurology" [1] Summary by Sections Technology Development - The research team utilized dynamic sports videos, specifically table tennis matches, as natural visual stimuli combined with virtual reality (VR) technology and machine learning algorithms to collect eye movement data without the need for active participation from subjects [1] - The study involved 25 participants, including 12 emotional disorder patients and 13 healthy controls, who watched table tennis and tennis match videos while their eye movement characteristics were recorded using the EyeKnow eye-tracking system [1] Research Findings - Significant testing and machine learning analysis revealed that 11 eye movement features from the table tennis videos effectively distinguished emotional disorders, with "Gaze Entropy" achieving an accuracy rate of 88% [2] - A decision tree model trained on all significant features reached an overall accuracy of 92% and an area under the ROC curve of 0.94, outperforming traditional assessment scales [2] - The tennis video showed weaker distinguishing effects due to lower familiarity among participants, but some indicators still demonstrated potential for adaptation to new content as a biological marker for emotional disorders [2] Future Applications - The core advantage of this technology lies in its "natural and unobtrusive" nature, requiring no active cooperation or professional guidance, allowing for detection simply through watching everyday videos [2] - Future integration into smart TVs and mobile devices is anticipated, enabling at-home mental health monitoring [2] - The research leader, Professor Tian Feng, emphasized that this method integrates disease screening into daily life, providing a low-cost and highly compliant long-term monitoring tool, particularly useful for tracking medication effects and relapse warnings [2]