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Gosavi Arundhati Tushar

dblp:374/8432 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0000-0002-8785-7335ORCID · 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 2021Human-computer interaction and ubiquitous computing · 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%
Human-computer interaction and pervasive computing
1 paper
Immersive interaction · 100%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Medical and health informatics · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
immersive interaction
1.012026
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
1.012026
A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026
Virtual and augmented reality
mixed reality
1.012026
A Mixed Reality System for Robust Manikin Localization in Childbirth Training · IEEE Trans. Vis. Comput. Graph. 2026
Virtual and augmented reality
tracking and registration
1.012026
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 · 2.0manikin comparison · 1.5empirical study · 1.5RGBD camera calibration · 1.0RGB-D camera calibration · 1.0
YearPublicationVenuePosition
2026 A Mixed Reality System for Robust Manikin Localization in Childbirth Training
abstract
Opportunities 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.5
2024 Facilitating Virtual Reality Integration in Medical Education: A Case Study of Acceptability and Learning Impact in Childbirth Delivery Training
abstract
Advancements in Virtual Reality (VR) technology have opened new frontiers in medical education, igniting interest among medical educators to incorporate it into mainstream curriculum, complementing traditional training modalities such as manikin training. Despite numerous VR simulators on the market, their uptake in medical education remains limited. This paper explores the acceptability and educational effectiveness of VR in the context of vaginal childbirth delivery training, with the simulator providing a walkthrough for the second and third stages of labour, contrasting it with established manikin-based methods. We conducted a large-scale empirical study with 117 medical students, revealing a significant 24.9% improvement in knowledge scores when using VR as compared to manikin. However, VR received significantly lower self-reported feasibility scores in Confidence, Usability, Enjoyment, Feedback and Presence, indicating low acceptance. The study provides critical insights into the relationship between technological innovation and educational impact, guiding future integration of VR into medical training curricula.
Chang Liu 0157, Felicia Fang-Yi Tan, Shengdong Zhao 0001, Abhiram Kanneganti, Gosavi Arundhati Tushar, Eng Tat Khoo
CHI5