Accurate characterization of dynamic joint stiffness (DJS) at the ankle is vital for understanding human locomotion and diagnosing clinical pathologies. Historically, researchers have relied on custom-molded fiberglass boots to interface between the participant and the testing apparatus. However, these traditional interfaces are hindered by lengthy fabrication times, participant discomfort, and mechanical compliances that can compromise signal accuracy.

To overcome these limitations, this project focuses on the design and validation of a novel, 3D-printed Sub-Malleolar Interface (SMI). To validate the SMI, a proof-of-concept study was conducted on a healthy participant, comparing the new device against a traditional fiberglass legacy boot. The experimental setup featured a high-torque electrohydraulic servomotor that applied pseudo-random arbitrary level distributed sequence (PRALDS) position perturbations to the ankle joint. The validation encompassed three distinct experimental paradigms: estimating the interface's moment of inertia, identifying DJS across eight quasi-stationary angles in a relaxed state, and identifying DJS during an isotonic plantarflexion contraction at 15% of maximum voluntary contraction (MVC). A parallel-cascade system identification method was then employed to mathematically decouple the resulting joint torque into its intrinsic (mechanical) and reflex (neural) components for comparative analysis.

The results demonstrated that the SMI is a robust and reliable alternative to traditional interfaces, producing intrinsic parameter estimates such as inertia, viscosity, and elasticity that closely matched those of the legacy boot. Total-torque predictions achieved high goodness-of-fit, with variance accounted for (VAF) reaching approximately 91% to 94% under relaxed conditions. Crucially, the SMI's enhanced mechanical coupling and reduced micro-slippage allowed it to capture reflex-mediated torques with greater fidelity than the more compliant fiberglass boot. By providing a standardized, highly efficient, and scalable interfacing solution, the SMI represents a significant advancement in the study of joint neuromechanics, paving the way for broader clinical translation and improved diagnostic capabilities for distinguishing neural spasticity from passive tissue stiffness