Exoskeletons, Exomusculatures, Exosuits: Dynamic Modeling and Simulation

Arslan, Yunus Ziya; Karabulut, Derya; Örteş, Faruk; Popović, Marko B.

doi:10.1016/b978-0-12-812939-5.00011-2

Cited by 11 publications

(11 citation statements)

References 49 publications

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“…The Hill model has enabled researchers to explore the mechanics of muscles using only a few rheological parameters. However, the Hill model falls short of elucidating the underlying biological mechanisms of force generation in muscles [89][90][91]. Another shortcoming of this model is that it fails to consider variations in the contractile characteristics of various fibre types within muscles and the dependence of muscle tension on the movement history [89,91].…”

Section: Muscle Contraction Defined By the Hill Modelmentioning

confidence: 99%

Active viscoelastic models for cell and tissue mechanics

Tajvidi Safa,

Huang,

Kabla

et al. 2024

R. Soc. Open Sci.

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Living cells are out of equilibrium active materials. Cell-generated forces are transmitted across the cytoskeleton network and to the extracellular environment. These active force interactions shape cellular mechanical behaviour, trigger mechano-sensing, regulate cell adaptation to the microenvironment and can affect disease outcomes. In recent years, the mechanobiology community has witnessed the emergence of many experimental and theoretical approaches to study cells as mechanically active materials. In this review, we highlight recent advancements in incorporating active characteristics of cellular behaviour at different length scales into classic viscoelastic models by either adding an active tension-generating element or adjusting the resting length of an elastic element in the model. Summarizing the two groups of approaches, we will review the formulation and application of these models to understand cellular adaptation mechanisms in response to various types of mechanical stimuli, such as the effect of extracellular matrix properties and external loadings or deformations.

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Section: Muscle Contraction Defined By the Hill Modelmentioning

confidence: 99%

Active viscoelastic models for cell and tissue mechanics

Tajvidi Safa,

Huang,

Kabla

et al. 2024

R. Soc. Open Sci.

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“…The musculotendon actuation links the muscle state and its activation to a produced force. A hill-type model is used to describe the contraction dynamics 16,33,37,38 and consists of three different elements replicating the force production function of the musculotendon system: the contractile element (CE), the passive elastic element (PEE), and serial elastic element (SEE). Each of these elements produces a force defined with normalized functions depending on the muscle intrinsic parameters as presented in the original model 21 .…”

Section: Contraction Dynamicsmentioning

confidence: 99%

A realistic upper-limb musculoskeletal model for overall muscle activation estimation

Barjavel

Proietti

Pierella

et al. 2023

Preprint

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The human upper-limb can perform complex tasks thanks to the ability of the central nervous system (CNS) to control and modulate the activation of more than 40 muscles. A deeper understanding of the strategies implemented by the CNS to perform these movements could help develop more effective rehabilitation protocols. Unfortunately, given the large number of muscles acting on the shoulder and the arm, and their positions with respect to each other, a simultaneous electromyographic (EMG) recording of all the upper-limb's muscles is not feasible in practice. Numerical musculoskeletal models could represent a useful alternative approach to gather this kind of information. Here, we develop a new realistic upper-limb biomechanical model which uses a combination of few recorded EMG data (up to max N=16) and an inverse dynamics model to predict the overall upper-limb muscle activations (N=42). With data from five healthy subjects performing 3D upper-limb movements, the predictions from this EMG-assisted model were compared to: 1) a full set of 16 arm muscles EMG data, 2) a model using none of the these recorded EMG data (standard static-optimization). While predicted signals from the EMG-assisted and static-optimization led to a 3.4% and 2.3% error respectively in the resulting moment of joint when fed into the movement dynamics, the EMG-assisted method presented a more physiological sharing of the muscle activations and variations of errors with respect of the activations from EMG signals for non used muscles in a comparison leave-one-out method. These results demonstrate the ability of the proposed musculoskeletal model to be used as a tool to evaluate healthy motion data, and open up to the application of such a strategy to analyze impaired individuals motion data and to compare inter-subject activation behaviours in the future.

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“…Moreover, the muscles are assumed homogeneous along their length and their force functions follow the well-known Hill-type model. As a result, the muscles ignore the impact of temporal dependency and the recent contractile conditions of the muscle on their generated force (Arslan et al, 2019).…”

Section: Limitations and Future Workmentioning

confidence: 99%