This study seeks to determine the efficacy of a novel, virtual reality (VR)-based sensorimotor assessment tool, VIST Neuro-ID, in comparison to the gold standard. This was achieved through computing common postural sway metrics, as well as comparing these metrics across population groups including sex and age (50–60 vs. 61–75). Sensorimotor assessments were conducted within the VIST Neuro-ID VR software while participants stood on a force plate. A proxy for center-of-pressure measurement was developed using the six-degree-of-freedom data collected from the head-mounted display used with the VR system. Moderate-to-high (r = 0.542–0.906) Pearson’s correlations were found between VIST Neuro-ID and the force plate for all eight postural sway metrics that were computed. Both systems detected significant differences (p < 0.05) across age groups for all metrics, except for two-dimensional path length from the force plate. Several significant differences were found across sexes, including AP and resultant sway velocity from the force plate, and resultant and AP root-mean-square from the HTC Vive Pro Eye. This indicates potential for VR to be used to collect vital postural sway metrics needed for assessing patient function, while also highlighting potential to identify balance patterns related to aging.

Digital twin technologies provide new opportunities for visualizing, analyzing, and managing complex physical systems; however, effective methods are needed to transform digital twin data into immersive environments that support human interaction and decision-making. This study presents the development of a Digital Twin Immersive Environment (DTIE) prototype for facility visualization and analysis at the U.S. Army Corps of Engineers Engineering Research and Development Center (ERDC) in Vicksburg, Mississippi. A photogrammetry-based workflow was used to capture the facility interior and generate a dense three-dimensional point cloud, while a separate Gaussian splat reconstruction was generated from the same image dataset. The Gaussian splat representation was spatially aligned with Revit-derived CAD geometry to provide complementary geometric and photorealistic representations within the immersive environment. A virtual reality (VR) environment was developed with a user interface (UI) supporting ray-cast interaction, speech-to-text data entry, multi-user collaboration, switching between CAD and Gaussian splat representations, in-scene web browsing, and occupant movement simulation. The resulting DTIE prototype demonstrates the technical integration of complementary CAD and Gaussian splat representations with immersive interaction capabilities for facility digital twins. The environment operated at approximately 71.8 FPS under the reported Meta Quest 2 configuration. This work establishes a prototype framework for future investigation of collaborative visualization, scenario analysis, and decision-support applications for complex infrastructure systems.

Introduction: The goal of this study is to determine whether trust in autonomous vehicles differs based on whether the vehicle operates in a closed or open system. This topic is increasingly important as autonomous vehicles become more integrated into everyday transportation environments. Understanding how pedestrians perceive and trust these systems can help guide safer deployment strategies, improve public acceptance, and support the development of transportation policies that reflect real-world behavioral expectations. Insights into trust dynamics across different system types can also assist designers and engineers in creating more predictable, transparent, and user-centered autonomous vehicle interactions. Methods: To examine these issues, the PRQF (pedestrian receptivity questionnaire for fully autonomous vehicles) survey was used, incorporating items that assessed pedestrian behavior, personal innovativeness, and receptivity to autonomous vehicles. Scenario-based questions were also included to evaluate differences in trust across open and closed settings. Results: The results showed higher pedestrian receptivity scores in the closed system (M = 14.11, SD = 3.78) than in the open system (M = 13.70, SD = 3.90). Average trust scores were likewise higher in the closed system (M = 4.68, SD = 1.82) compared to the open system (M = 4.56, SD = 1.85). Discussion: These findings indicate that both trust and receptivity toward autonomous vehicles are greater in closed systems. Future policies around autonomous vehicles can incorporate the use of closed systems for autonomous vehicle use to improve user trust and receptivity.