Improving the Temporal Accuracy of Eye Gaze Tracking for the da Vinci Surgical System through Automatic Detection of Decalibration Events and Recalibration
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Le résumé fourni par la source
Robust and accurate eye gaze tracking can advance medical telerobotics by providing complementary data for surgical training, interactive instrument control, and augmented human–robot interactions. However, current gaze tracking solutions for systems such as the da Vinci Surgical System (dVSS) are limited to complex hardware installations. Additionally, existing methods do not account for operator head movement inside the surgeon console, invalidating the original calibration. This work provides an initial solution to these challenges that can seamlessly integrate into console devices beyond the dVSS. Our approach relies on simple and unobtrusive wearable eye tracking glasses and provides calibration routines that can contend with operator-head movements. An external camera measures movement of the glasses through trackers mounted on the glasses to detect invalidation of the prior calibration from head movement and slippage. Movements beyond a threshold of 5 cm or 9ˆ∘ prompt another calibration sequence. In a study where users moved freely in the surgeon console after an initial calibration procedure, we show that our system tracks the eye tracking glasses to initiate recalibration procedures. Recalibration can reduce the mean tracking error up to 89% compared to the current prevailing approach which relies on the initial calibration only. This work is an important first step towards incorporating user movement into gaze-based applications for the dVSS.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Improving the Temporal Accuracy of Eye Gaze Tracking for the da Vinci Surgical System through Automatic Detection of Decalibration Events and Recalibration
- Date Crossref
- 19/03/2024
- Éditeur
- World Scientific Pub Co Pte Ltd
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Johns Hopkins University Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Case Western Reserve University Department of Electrical pays non établi dans la noticeUniversité ou école supérieure
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Vanderbilt University Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Friedrich-Alexander-Universität Erlangen-Nürnberg pays non établi dans la noticeUniversité ou école supérieure
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Johns Hopkins Medical Institute Department of Urology pays non établi dans la noticeStructure de recherche
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Friedrich-Alexander University Erlangen-Nuremberg Pattern Recognition Lab pays non établi dans la noticeUniversité ou école supérieure
Department of Computer Science — Johns Hopkins University, Department of Electrical — Case Western Reserve University et Department of Computer Science — Vanderbilt University, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.