Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle’s pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units’ stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study’s relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.
|Title of host publication||Converging Clinical and Engineering Research on Neurorehabilitation IV|
|Subtitle of host publication||Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020|
|Editors||Diego Torricelli, Metin Akay, Jose L. Pons|
|Publication status||Published - 2022|
|Event||ICNR 2020: International Conference on NeuroRehabilitation (Virtual) - |
Duration: 13 Oct 2020 → 16 Oct 2020
|Name||Biosystems and Biorobotics|
|Conference||ICNR 2020: International Conference on NeuroRehabilitation (Virtual)|
|Period||13/10/20 → 16/10/20|
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