Visualization of Tracking Uncertainty in AR-based Surgical Guidance

要旨

Uncertainty caused by instrument tracking errors affects critical tasks such as surgery assisted by Augmented Reality (AR) guidance. This work investigates whether visualizing such uncertainty can improve task performance, trust, and confidence. We present four visualization techniques: Cone, Circle, Gauge, and Color. A two-part study evaluated these techniques on a surgical drilling task, first with 24 non-professional participants and then with 4 professional surgeons. Results indicate that uncertainty visualization improved drilling accuracy by 24% but increased task time by 76%. It also enhanced user confidence and trust in the system, with Cone and Circle as the most preferred visualizations. Based on our findings, we discuss design recommendations for integrating uncertainty visualization into AR-based surgical systems. This work paves the way for a higher success rate in surgical procedures.

著者
Chaymae Acherki
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
Laurence Nigay
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
Quentin Roy
Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
Thibault Salque
AREAS, Grenoble, France
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Health Tools and Technologies

P1 - Room 129
7 件の発表
2026-04-16 18:00:00
2026-04-16 19:30:00