ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Biomedical Robotics
This article is part of the Research TopicAdvances in Smart and Adaptive Prosthetic and Wearable TechnologiesView all articles
Modeling the Biomechanical Features Affecting the Metabolic Rate of Walking with a Powered Ankle-Foot Prosthesis
Provisionally accepted
- 1Department of Mechanical Engineering, University of Colorado Boulder, Boulder, United States
- 2Department of Integrative Physiology, University of Colorado Boulder, Boulder, United States
- 3Department of Veterans Affairs, Eastern Colorado Healthcare System, Denver, CO, United States
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For individuals with unilateral transtibial amputation, powered ankle-foot prostheses have the potential to reduce the metabolic rate of walking, which could contribute to improvements in mobility and quality of life; however, physiological improvements have not been consistently demonstrated in experimental studies. To improve our understanding of the biomechanical mechanisms that drive metabolic rate outcomes, we used a machine learning approach to model the relationship between multimodal biomechanical factors and the metabolic rate of walking with a powered ankle-foot prosthesis. Our model included 50 features describing spatiotemporal parameters, step-to-step transition work, joint kinematics, muscle activity, ground reaction forces, prosthesis settings, and subject characteristics, and resulted in a pseudo-R2 of 0.986. The features with the largest effect on metabolic rate were peak unaffected side ankle inversion angle, leading affected leg positive work during the step-to-step transition, and peak affected knee extension angle. Accumulated local effects plots were used to visualize the direction and magnitude of the relationship between each feature and the metabolic rate of walking. This work furthers our knowledge about the biomechanical and physiological response to powered ankle-foot prosthesis use and could assist in developing new strategies to drive reductions in metabolic rate.
Keywords: lower-limb biomechanics, Gait, transtibial amputation, machine learning, Energy Expenditure
Received: 18 Sep 2025; Accepted: 25 Nov 2025.
Copyright: © 2025 Schneider, Colvin, Grabowski and Welker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mikayla Schneider
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