A multimodal biomechanics and electromyography dataset of drop vertical jump under virtual-reality visual height perturbations in individuals with and without chronic ankle instability

This dataset contains multimodal biomechanical data of drop vertical jump (DVJ) performed under controlled virtual-reality (VR) visual height perturbations. Data were collected from 28 participants, including individuals with chronic ankle instability (CAI) and healthy control participants. Each participant completed one real-world control condition (30 cm) and four VR visual-height conditions (0, 10, 30 and 50 cm), in which the physical drop height was fixed while the perceived visual height was manipulated. Synchronous whole-body kinematics, ground reaction forces and bilateral surface electromyography (EMG) signals from key lower-limb muscles were recorded during each trial. Motion capture data were collected using a 38-marker Plug-in-Gait model. The dataset provides raw and processed data in C3D, OpenSim-compatible (.trc and .mot), and ASCII formats, together with participant-level metadata including demographics and lower-limb anthropometrics. This dataset can be reused to investigate jump–landing biomechanics under visual perturbation, perception–action coupling, musculoskeletal simulation, inverse dynamics analysis, and data-driven modeling in CAI and non-CAI populations.