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ABSTRACT This paper introduces a novel computational framework for evaluating above‐knee prostheses, addressing a major challenge in gait deviation studies: distinguishing between prosthesis‐specific and patient‐specific contributions to gait deviations. This innovative approach utilizes three separate computational models to quantify the changes in gait dynamics necessary to achieve a set of ideal gait kinematics across different prosthesis designs. The pilot study presented here employs a simple two‐dimensional swing‐phase model to conceptually demonstrate how the outcomes of this three‐model framework can assess the extent to which prosthesis design impacts a user's ability to replicate the dynamics of able‐bodied gait. Furthermore, this framework offers potential for optimizing passive prosthetic devices for individual patients, thereby reducing the need for real‐life experiments, clinic visits, and overcoming rehabilitation challenges. 

Background/Objectives: This study explores an optimization-based strategy for muscle force estimation by employing simplified cost functions integrated with physiologically relevant muscle models. Methods: Considering elbow flexion as a case study, we employ an inverse-dynamics approach to estimate muscle forces for the biceps brachii, brachialis, and brachioradialis, utilizing different combinations of cost functions and muscle constitutive models. Muscle force generation is modeled by accounting for active and passive contractile behavior to varying degrees using Hill-type models. In total, three separate cost functions (minimization of total muscle force, mechanical work, and muscle stress) are evaluated with each muscle force model to represent potential neuromuscular control strategies without relying on electromyography (EMG) data, thereby characterizing the interplay between muscle models and cost functions. Results: Among the evaluated models, the Hill-type muscle model that incorporates both active and passive properties, combined with the stress minimization cost function, provided the most accurate predictions of muscle activation and force production for all three arm flexor muscles. Our results, validated against existing biomechanical data, demonstrate that even simplified cost functions, when paired with detailed muscle models, can achieve high accuracy in predicting muscle forces. Conclusions: This approach offers a versatile, EMG-free alternative for estimating muscle recruitment and force production, providing a more accessible and adaptable tool for muscle force analysis. It has profound implications for enhancing rehabilitation protocols and athletic training, not only broadening the applicability of muscle force estimation in clinical and sports settings but also paving the way for future innovations in biomechanical research.