Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mahesh Arjandas Choolani
dblp:429/6523
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0001-8336-0973ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Virtual and augmented reality · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
immersive interaction |
1.0 | 1 | 2026 | A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026 |
Virtual and augmented reality › medical virtual reality
medical training simulator |
1.0 | 1 | 2026 | A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026 |
Virtual and augmented reality
mixed reality |
1.0 | 1 | 2026 | A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026 |
Virtual and augmented reality
tracking and registration |
1.0 | 1 | 2026 | A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
fiducial marker tracking · 2.0coarse-to-fine localization · 2.0RGBD camera calibration · 1.0RGB-D camera calibration · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Mixed Reality System for Robust Manikin Localization in Childbirth TrainingabstractOpportunities for medical students to gain practical experience in vaginal births are increasingly constrained by shortened clinical rotations, patient reluctance, and the unpredictable nature of labour. To alleviate clinicians' instructional burden and enhance trainees' learning efficiency, we introduce a mixed reality (MR) system for childbirth training that combines virtual guidance with tactile manikin interaction, thereby preserving authentic haptic feedback while enabling independent practice without continuous on-site expert supervision. The system extends the passthrough capability of commercial head-mounted displays (HMDs) by spatially calibrating an external RGB-D camera, allowing real-time visual integration of physical training objects. Building on this capability, we implement a coarse-to-fine localization pipeline that first aligns the maternal manikin with fiducial markers to define a delivery region and then registers the pre-scanned neonatal head within this area. This process enables spatially accurate overlay of virtual guiding hands near the manikin, allowing trainees to follow expert trajectories reinforced by haptic interaction. Experimental evaluations demonstrate that the system achieves accurate and stable manikin localization on a standalone headset, ensuring practical deployment without external computing resources. A large-scale user study involving 83 fourth-year medical students was subsequently conducted to compare MR-based and virtual reality (VR)-based childbirth training. Four senior obstetricians independently assessed performance using standardized criteria. Results showed that MR training achieved significantly higher scores in delivery, post-delivery, and overall task performance, and was consistently preferred by trainees over VR training. Although validated only in the context of obstetric delivery, the system demonstrates strong potential for broader manikin-based procedural training and other healthcare education scenarios. Haojie Cheng, Chang Liu 0157, Abhiram Kanneganti, Mahesh Arjandas Choolani, Gosavi Arundhati Tushar, Eng Tat Khoo |
IEEE Trans. Vis. Comput. Graph. | 4 |