Metabolic energy expenditure is a critical performance measure of human motion, and its implication has a broad range of scientific, clinical, and engineering applications. In particular, assessing metabolic cost of prosthetic gait is important for diagnosis, design, and usability tests. This award supports establishing a novel joint-based model that evaluates metabolic energy expenditure without the limitations inherent in common experimental measurements (time delays, limited task and range of motion due to equipment) or traditional muscle-based models (excessive number of unknown parameters, complex interactions between muscles and the surroundings, reliance on non-active human parameters). Given the increasing demand and large number of people (more than 173,000 users in the U.S.) who would benefit from lower-limb prostheses, the model implementation into prosthetic design optimization will have broad social impacts by resolving the major hurdles to their wide-spread use through reduced metabolic cost and fatigue. Also, the reduced development cost due to reduced usability tests will make products available to more economically diverse communities. The collaborations between engineering and clinical disciplines will be mutually beneficial and enhance networks and partnerships for research and education, which will involve underrepresented (race, gender, disability) groups at various stages. The calculation algorithm will be open-sourced for the prostheses developers in industry and academia.
The goals of this project are to establish a joint-space dynamic model of metabolic energy expenditure by integrating the laws of thermodynamics and the principles of multibody system dynamics, and implement the model for energy-efficient prosthetic gait. Specific objectives are to: (1) derive a general dynamic model of metabolic energy expenditure and identify and validate the joint-space model parameters from experiments; (2) introduce a modeling and identification framework of metabolic cost for locomotion of individuals with transtibial prostheses and experimentally validate; and (3) implement the models into a nonlinear programming algorithm to optimize the lower-limb prosthetic alignment for minimum metabolic cost of prosthetic gait, and experimentally demonstrate proof-of-concept. The muscle energetic properties will be mapped to the joint space, and the energetic effects of negative work, intra/inter-joint coupling, and muscle co-contraction will be modeled. The joint-space parameters estimated from active human subjects will provide the model with improved accuracy and efficient calculations, which can be used to evaluate metabolic cost for complex non-periodic functional tasks that may not be experimentally verifiable by traditional means. The model also enables real-time calculations of instantaneous metabolic rate as a function of time.
