Virtual Reality-Simulated Interaction Between Micro-Mobility Vehicles and Pedestrians: A Biomechanical Analysis of Human Gait and Movement Responses

Sahan Siriwardena, Omar Aboelrous, Anas Madkoor, Mohamed Elansari, Aiman Alhetari, Jassim Al Jufairi, Mohammed Al-Kuwari, Qinaat Hussain, Charitha Dias, Ajith Sominanda Herath

Pedestrian walking is a fundamental activity of daily living and a key component of first and last-mile urban mobility. The rapid adoption of e-scooters has increased pedestrian-vehicle interactions on shared sidewalks and crossings, raising collision risks. However, most previous studies have relied on trajectory-based observations, providing limited insight into biomechanical gait responses. This study investigated pedestrian gait adaptations during simulated e-scooter interactions using immersive virtual reality (VR) and markerless pose estimation. Twelve healthy male university students (21-23 years) completed four VR walking scenarios: normal walking, e-scooter encounters at 10-25 km/h, crossing encounters, and near-crash encounters. Sagittal-plane videos were analyzed using the OpenPose 25-point model. Step length, gait cycle time, walking velocity, stance and swing phases, and lower-limb joint trajectories were extracted using Kinovea and custom JSON-based analysis tools. Statistical analyses included ANOVA, MANOVA, and non-parametric tests Crossing and near-crash scenarios significantly reduced step length (p<0.001), from 226.5 cm during normal walking to 204.7 cm during near-crash simulations. Although gait velocity and timing were not significantly affected, participants consistently exhibited shorter stance phases, longer swing phases, and restricted knee motion during stressful encounters, indicating reflexive gait adaptations to perceived collision risk. These findings demonstrate that immersive VR combined with markerless pose estimation effectively quantifies pedestrian biomechanical responses to micro-mobility interactions. Gait adaptations identified in this study may serve as sensitive indicators of collision risk and support the development of proactive pedestrian safety measures and intelligent micro-mobility control systems.