Wearable sensors reveal the reality gap: a full-body motion dataset for validating VR cycling simulators

Virtual reality cycling simulators are increasingly used in research on urban mobility, rehabilitation, and road safety, yet their biomechanical fidelity remains under-validated due to the lack of standardized, publicly available datasets. We present a novel benchmark resource comprising synchronized full-body inertial measurement unit (IMU) data at 120 Hz and egocentric video from 10 participants who cycled an identical 1.4 km urban route in both Vienna and a motion-enabled VR simulator. Six body-worn sensors captured head, torso, arm, and leg movements, enabling detailed comparisons of pedaling rhythm, limb coordination, balance control, and visual attention patterns across environments. Our analyses reveal that VR successfully replicates fundamental locomotor patterns including pedaling cadence and bilateral leg coordination. However, significant differences emerge in upper-body dynamics: participants exhibited greater torso rotational variability in VR compared to real-world cycling, suggesting altered balance strategies. Head movement patterns showed broader yaw angles during virtual riding, yet these differences did not correlate with simulator sickness or immersion ratings. Emotional assessments indicated lower enjoyment and higher frustration in VR, highlighting gaps in affective realism despite mechanical similarity. This openly available dataset provides a critical resource for validating simulator designs, developing personalized VR training systems, and advancing research in embodied locomotion. The dataset, analysis tools, and documentation are freely accessible at: https://street2simulator.de/ .