{"id":32181,"date":"2026-01-21T17:01:27","date_gmt":"2026-01-21T17:01:27","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/towards-automated-analysis-of-gaze-behavior-from-consumer-vr-devices-for-neurological-diagnosis\/"},"modified":"2026-01-21T17:19:32","modified_gmt":"2026-01-21T17:19:32","slug":"towards-automated-analysis-of-gaze-behavior-from-consumer-vr-devices-for-neurological-diagnosis","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/towards-automated-analysis-of-gaze-behavior-from-consumer-vr-devices-for-neurological-diagnosis\/","title":{"rendered":"Towards Automated Analysis of Gaze Behavior from Consumer VR Devices for Neurological Diagnosis"},"content":{"rendered":"<p>Recent studies have demonstrated that eye tracking is a valuable tool in the detection, classification and staging of neurodegenerative diseases such as Parkinson\u2019s Disease<\/p>\n<p>(PD). However, traditional methods for capturing gaze data often rely on expensive and non-engaging clinical equipment such as video-oculography, limiting their accessibility and<\/p>\n<p>scalability. In this work, we investigate the feasibility of using eye tracking data collected via consumer-grade virtual reality (VR) headsets to support neurological diagnostics in a<\/p>\n<p>more accessible and user-friendly manner.<\/p>\n<p>This approach enables large-scale, low-cost, and remote assessments, which are particularly valuable in early detection and monitoring of neurodegenerative conditions. We show<\/p>\n<p>that relevant oculomotor features extracted from VR-based eye tracking can be used for predictive assessment. Despite the inherent noise and lower precision of consumer devices,<\/p>\n<p>careful preprocessing and robust feature engineering, including deep learning embeddings, mitigate these limitations. Our results demonstrate that both handcrafted and learned fea<\/p>\n<p>tures from gaze behavior enable promising levels of classification performance. This research represents an important step towards scalable, automated, and accessible diagnostic tools for neurodegenerative diseases using ubiquitous VR technology.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recent studies have demonstrated that eye tracking is a valuable tool in the detection, classification and staging of neurodegenerative diseases such as Parkinson\u2019s Disease (PD). However, traditional methods for capturing gaze data often rely on expensive and non-engaging clinical equipment such as video-oculography, limiting their accessibility and scalability. In this work, we investigate the feasibility of using eye tracking data collected via consumer-grade virtual reality (VR) headsets to support neurological [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32181","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32181","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32181\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32181"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32181"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}