A Novel Computational Framework for Identifying Prosthesis-Specific & Patient-Specific Contributions to Gait Deviations

People with above-knee (transfemoral) amputations often walk with abnormal patterns that can lead to secondary health issues such as joint pain, arthritis, and back problems. While improvements in prostheses designs and subject-specific training strategies can help mitigate these risks, current gait evaluation methods are limited in their ability to distinguish between contributions from the prosthesis and the patient.

This work presents a novel computational framework designed to overcome these limitations by isolating prosthesis-specific effects on gait dynamics. The approach employs three musculoskeletal models: a fully able-bodied model, an ideal prosthesis model, and a full prosthesis model; all driven by identical reference joint kinematics (angular positions, velocities, and accelerations at the hip, knee, ankle, etc.). By systematically comparing the joint kinetics (forces and moments) across these models, the framework identifies the mechanical demands introduced by prosthesis design and quantifies deviations resulting from patient adaptations.

A pilot study demonstrates the framework’s capability using simplified swing-phase models, highlighting differences in joint torque profiles due to prosthesis inertial properties and mechanical constraints. Optimization techniques are then employed to identify prosthesis configurations that best replicate able-bodied motion. Current work lays the foundation for clinically relevant tools that enable more effective prosthesis tuning and personalized rehabilitation strategies without requiring extensive patient testing. It has the potential to improve rehabilitation outcomes, reduce injury risk, and help clinicians better tailor prosthetic solutions for each individual.