Methodological Framework for the Parametric Characterization of Ankle Kinematics in Inverse Dynamics–Based Human–Structure Interaction Models

• Proposes a framework linking intrinsic gait parameters to 2D ankle kinematics. • Synthesizes trajectories, eliminating subject-specific experimental calibration. • Uses confidence bands to capture non-deterministic cycle-to-cycle variability. • Provides efficient kinematic inputs for inverse dynamics HSI simulations. This paper proposes a three-stage methodological framework for the parametric synthesis of ankle kinematics, specifically designed for integration into inverse dynamics–based human–structure interaction (HSI) models. The procedure establishes a standardized workflow comprising: (i) smartphone-based experimental motion capture, (ii) systematic identification of gait phases through vectorial trajectory processing, and (iii) the formulation of a scalable numerical architecture based on intrinsic parameters. Key achievements of this research include the effective mapping of walking speed and subject-specific anthropometry into parametric scaling functions, enabling the synthesis of subject-dependent motion profiles without requiring personalized experimental campaigns. The resulting model describes the foot rollover angle and spatial ankle trajectories during the stance phase by linking anthropometric reference values with normalized background curves. Validation of the framework against experimental measurements demonstrates high fidelity in capturing kinematic trends, yielding representative relative errors of 1.01% for the foot rollover angle and below 9.4% for spatial trajectories. Furthermore, the incorporation of intra-subject variability through confidence bands allows for a realistic, non-deterministic representation of consecutive gait cycles, addressing the inherent variability of human locomotion. These results confirm the framework as a robust, computationally efficient, and modular tool that enhances the accuracy of structural serviceability assessments by providing physically consistent kinematic inputs across diverse pedestrian profiles.