Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking
Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularl...
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2016
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| Online Access: | http://hdl.handle.net/1721.1/103392 https://orcid.org/0000-0003-3169-1011 |
| _version_ | 1810974004412940288 |
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| author | Markowitz, Jared John Herr, Hugh M. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Markowitz, Jared John Herr, Hugh M. |
| author_sort | Markowitz, Jared John |
| collection | MIT |
| description | Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle. |
| first_indexed | 2024-09-23T08:16:35Z |
| format | Article |
| id | mit-1721.1/103392 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:16:35Z |
| publishDate | 2016 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/1033922022-09-23T12:05:46Z Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking Markowitz, Jared John Herr, Hugh M. Massachusetts Institute of Technology. Media Laboratory Markowitz, Jared John Herr, Hugh M. Humans employ a high degree of redundancy in joint actuation, with different combinations of muscle and tendon action providing the same net joint torque. Both the resolution of these redundancies and the energetics of such systems depend on the dynamic properties of muscles and tendons, particularly their force-length relations. Current walking models that use stock parameters when simulating muscle-tendon dynamics tend to significantly overestimate metabolic consumption, perhaps because they do not adequately consider the role of elasticity. As an alternative, we posit that the muscle-tendon morphology of the human leg has evolved to maximize the metabolic efficiency of walking at self-selected speed. We use a data-driven approach to evaluate this hypothesis, utilizing kinematic, kinetic, electromyographic (EMG), and metabolic data taken from five participants walking at self-selected speed. The kinematic and kinetic data are used to estimate muscle-tendon lengths, muscle moment arms, and joint moments while the EMG data are used to estimate muscle activations. For each subject we perform an optimization using prescribed skeletal kinematics, varying the parameters that govern the force-length curve of each tendon as well as the strength and optimal fiber length of each muscle while seeking to simultaneously minimize metabolic cost and maximize agreement with the estimated joint moments. We find that the metabolic cost of transport (MCOT) values of our participants may be correctly matched (on average 0.36±0.02 predicted, 0.35±0.02 measured) with acceptable joint torque fidelity through application of a single constraint to the muscle metabolic budget. The associated optimal muscle-tendon parameter sets allow us to estimate the forces and states of individual muscles, resolving redundancies in joint actuation and lending insight into the potential roles and control objectives of the muscles of the leg throughout the gait cycle. United States. National Aeronautics and Space Administration (grant number 6926843) 2016-06-30T19:18:28Z 2016-06-30T19:18:28Z 2016-05 2015-09 Article http://purl.org/eprint/type/JournalArticle 1553-7358 http://hdl.handle.net/1721.1/103392 Markowitz, Jared, and Hugh Herr. “Human Leg Model Predicts Muscle Forces, States, and Energetics During Walking.” Edited by Adrian M Haith. PLoS Comput Biol 12, no. 5 (May 13, 2016): e1004912. https://orcid.org/0000-0003-3169-1011 en_US http://dx.doi.org/10.1371/journal.pcbi.1004912 PLOS Computational Biology Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science PLOS |
| spellingShingle | Markowitz, Jared John Herr, Hugh M. Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title | Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title_full | Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title_fullStr | Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title_full_unstemmed | Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title_short | Human Leg Model Predicts Muscle Forces, States, and Energetics during Walking |
| title_sort | human leg model predicts muscle forces states and energetics during walking |
| url | http://hdl.handle.net/1721.1/103392 https://orcid.org/0000-0003-3169-1011 |
| work_keys_str_mv | AT markowitzjaredjohn humanlegmodelpredictsmuscleforcesstatesandenergeticsduringwalking AT herrhughm humanlegmodelpredictsmuscleforcesstatesandenergeticsduringwalking |