Validate the force-velocity relation of the Hill’s muscle model from a molecular perspective

Zhao, Yongkun; Ding, Shihang; Todoh, Masahiro

doi:10.3389/fbioe.2022.1006571

Cited by 4 publications

(5 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This hyperbolic force–velocity relationship of muscle has been classically regarded as a pure empirical description of the macroscopic force–velocity data 27 , 67 . However, recent works have established the relationship between the mechanical manifestation in terms of force–velocity data and the kinetics of the cross-bridge cycle driven by ATP hydrolysis, describing how the molecular events within such a process can be transformed into the hyperbolic Hill equation 68 , 69 .…”

Section: Discussionmentioning

confidence: 99%

Load sharing between synergistic muscles characterized by a ligand-binding approach and elastography

Grinspan,

Fernandes de Oliveira,

Brandao

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The skeletal muscle contraction is determined by cross-bridge formation between the myosin heads and the actin active sites. When the muscle contracts, it shortens, increasing its longitudinal shear elastic modulus ($${\mu }_{L}$$ μ L ). Structurally, skeletal muscle can be considered analogous to the molecular receptors that form receptor–ligand complexes and exhibit specific ligand-binding dynamics. In this context, this work aims to apply elastography and the ligand-binding framework to approach the possible intrinsic mechanisms behind muscle synergism. Based on the short-range stiffness principle and the acoustic–elasticity theory, we define the coefficient $$C$$ C , which is directly related to the fraction saturation of molecular receptors and links the relative longitudinal deformation of the muscle to its $${\mu }_{L}$$ μ L . We show that such a coefficient can be obtained directly from $${\mu }_{L}$$ μ L estimates, thus calculating it for the biceps brachii, brachioradialis, and brachialis muscles during isometric elbow flexion torque (τ) ramps. The resulting $$C\left(\tau \right)$$ C τ curves were analyzed by conventional characterization methods of receptor–ligand systems to study the dynamical behavior of each muscle. The results showed that, depending on muscle, $$C\left(\tau \right)$$ C τ exhibits typical ligand-binding dynamics during joint torque production. Therefore, the above indicates that these different behaviors describe the longitudinal shortening pattern of each muscle during load sharing. As a plausible interpretation, we suggested that this could be related to the binding kinetics of the cross-bridges during their synergistic action as torque increases. Likewise, it shows that elastography could be useful to assess contractile processes at different scales related to the change in the mechanical properties of skeletal muscle.

show abstract

Section: Discussionmentioning

confidence: 99%

Load sharing between synergistic muscles characterized by a ligand-binding approach and elastography

Grinspan,

Fernandes de Oliveira,

Brandao

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The behavior of skeletal muscles is strongly nonlinear; to model it, a Hill-based muscle modeling method is used here, which is validated for a good description of the force–velocity relation of skeletal muscle by our previous study [ 19 ]. The Hill-based muscle model consists of three parts: contractile, series elastic, and parallel elastic components.…”

Section: Musculoskeletal Arm Modelmentioning

confidence: 99%

“… The diagram of the Hill-based muscle model, which is composed of the CC, PEC, and SEC [ 19 ], where

and

are the muscle tendon force and muscle fiber force, and

is the pennation angle, which is the angle between the direction of muscle fibers and the direction of muscle behaviors. …”

Section: Figurementioning

confidence: 99%

See 1 more Smart Citation

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements

Zhao

Zhang

et al. 2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller’s muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.

show abstract

“…Muscle force and joint moment during the STS movement are an important basis for evaluating patients’ physical health. The Hill muscle model is the most common method to calculate muscle force; however, there are few studies on further calculation of joint moment during the STS movement after calculating muscle force [ 2 , 3 ]. Lower limb joint moment, especially ankle joint moment, is an important indicator for evaluating STS stability of human motion [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Ankle Muscle Dynamics during the STS Process Based on Wearable Sensors

Liu

et al. 2023

Sensors

View full text Add to dashboard Cite

Ankle joint moment is an important indicator for evaluating the stability of the human body during the sit-to-stand (STS) movement, so a method to analyze ankle joint moment is needed. In this study, a wearable sensor system that could derive surface-electromyography (sEMG) signals and kinematic signals on the lower limbs was developed for non-invasive estimation of ankle muscle dynamics during the STS movement. Based on the established ankle joint musculoskeletal information and sEMG signals, ankle joint moment during the STS movement was calculated. In addition, based on a four-segment STS dynamic model and kinematic signals, ankle joint moment during the STS movement was calculated using the inverse dynamics method. Ten healthy young people participated in the experiment, who wore a self-developed wearable sensor system and performed STS movements as an experimental task. The results showed that there was a high correlation (all R ≥ 0.88) between the results of the two methods for estimating ankle joint moment. The research in this paper can provide theoretical support for the development of an intelligent bionic joint actuator and clinical rehabilitation evaluation.

show abstract

Validate the force-velocity relation of the Hill’s muscle model from a molecular perspective

Cited by 4 publications

References 54 publications

Load sharing between synergistic muscles characterized by a ligand-binding approach and elastography

Load sharing between synergistic muscles characterized by a ligand-binding approach and elastography

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements

Analysis of Ankle Muscle Dynamics during the STS Process Based on Wearable Sensors

Contact Info

Product

Resources

About