Related Experiment Video
Updated: Jun 22, 2025
05:12
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
Published on: August 12, 2021
2.0K
Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices
Valentyn Stadnytskyi1, Bahaa Ghammraoui1
1Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
Sensors (Basel, Switzerland)
|June 27, 2024
Summary
A new automated setup precisely evaluates augmented and virtual reality (AR/VR) depth sensors for healthcare regulations. This system ensures reliable performance characterization of extended reality (XR) technologies in simulated clinical settings.
Area of Science:
- Engineering
- Medical Technology
- Computer Science
Background:
- Augmented and virtual reality (AR/VR) technologies are increasingly adopted in healthcare.
- Regulatory evaluation of AR/VR systems requires precise characterization of their components, particularly depth sensors.
- Existing methods for depth sensor evaluation may lack the automation and controlled simulation capabilities needed for healthcare applications.
Purpose of the Study:
- To present a fully automated, modular benchtop experimental setup for quantitative evaluation of depth sensors used in extended reality (XR) technologies.
- To enable regulatory assessment of AR/VR devices in healthcare by simulating realistic scenarios for head-mounted displays.
- To characterize the performance of depth sensors, focusing on spatial resolution, Z-accuracy, and pixel-to-pixel correlation.
Main Methods:
- Development of a modular benchtop platform with a three-degree-of-freedom motorized observation system and a test object stage.
- Integration of various sensors, including range-sensing cameras and commercial AR headsets (e.g., Intel RealSense L515 LiDAR camera).
- Implementation of an automated data collection process for quantitative analysis in a controlled environment.
Main Results:
- Demonstration of the setup's capability to perform quantitative analysis of depth cameras for XR technologies.
- Evaluation studies revealed insights into spatial resolution, Z-accuracy, and pixel-to-pixel correlation of depth sensors.
- The system successfully simulated realistic scenarios for head-mounted displays in healthcare contexts.
Conclusions:
- The developed automated experimental setup provides a reliable method for characterizing depth sensors in AR/VR systems for healthcare.
- This quantitative evaluation approach supports regulatory compliance and ensures the precision of XR technologies in clinical settings.
- The findings offer valuable data on the performance of depth sensing in simulated healthcare environments, paving the way for safer and more effective AR/VR integration.
