A Mixed Reality System for Robust Manikin Localization in Childbirth Training
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Abstract (arXiv)
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.
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References · 11
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- 2022NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimationcited by 57
- 2025Any6D: Model-free 6D Pose Estimation of Novel Objectscited by 47
- 2020HoloLens 2 Research Mode as a Tool for Computer Vision Researchcited by 34
- 2020Towards human-level performance on automatic pose estimation of infant spontaneous movementscited by 10
- 2022Under the Cover Infant Pose Estimation using Multimodal Datacited by 6
- 2023HoloPOCUS: Portable Mixed-Reality 3D Ultrasound Tracking, Reconstruction and Overlaycited by 2
- 2023Realistic Volume Rendering with Environment-Synced Illumination in Mixed Realitycited by 2
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