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CHI2026顶会

Visualization of Tracking Uncertainty in AR-based Surgical Guidance

Chaymae Acherki, Laurence Nigay, Quentin Roy, Thibault Salque

2026年份
1被引次数

摘要

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.

• Human-centered computing → HCI design and evaluation methods; Usability testing.

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