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

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

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 ([Formula: see text]). 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 [Formula: see text], which is directly related to the fraction saturation of molecular receptors and links the relative longitudinal deformation of the muscle to its [Formula: see text]. We show that such a coefficient can be obtained directly from [Formula: see text] estimates, thus calculating it for the biceps brachii, brachioradialis, and brachialis muscles during isometric elbow flexion torque (τ) ramps. The resulting [Formula: see text] 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, [Formula: see text] 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.

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

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.

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements

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.