This study introduces a physics-based framework for modeling human running biomechanics by interpreting footstrike events as point-source excitations generating radially propagating wavefronts, akin to A0-mode Lamb waves, in a cylindrical coordinate system. Using a two-dimensional damped wave equation solved via finite-difference methods, we simulate spatiotemporal displacement fields and compare the outcomes with realworld gait kinematic and kinetic data. Our approach performs a parameter sweep of excitation frequency and amplitude to identify configurations closely replicating biomechanical signals associated with different running profiles and injury states. Unlike traditional machine learning approaches, our model leverages physical wave dynamics for simulation-validation matching, enabling interpretable identification of anomalies and potential injury risks. The results reveal distinctive wave propagation patterns between injured and non-injured runners, supporting the viability of wave-based modeling as a diagnostic and analytic tool in sports biomechanics. This work opens a novel direction for physics-informed, data-driven hybrid methods in gait analysis and injury prevention.

Keywords—Biomechanics; foot-strike modeling; lamb waves; wave equation; gait analysis; Internet of Things (IoT); HumanComputer Interaction (HCI)