Neuromuscular Dynamics Research Articles

Biomechanical and Neuromuscular Insights into Deadlift Variations: Implications for Sports Science, Strength Training, and Rehabilitation

The deadlift is a fundamental exercise in resistance training, essential for the development of overall strength and power. This review synthesizes current research on kinematics and electromyographic (EMG) activity during deadlifts, highlighting the effects of different variations and techniques on performance and muscle activation. Kinematic studies have revealed significant differences in joint angles and movement patterns between conventional and sumo deadlifts, emphasizing the importance of technique and experience in optimizing performance and reducing injury risk. EMG analysis has also revealed distinct muscle activation profiles for key muscles, such as the vastus lateralis, gluteus maximus, and hamstrings, across different deadlift variations. These findings are critical for designing effective, individualized training programs in strength and conditioning, as well as developing targeted rehabilitation and injury prevention strategies in sports medicine. By understanding the biomechanical and neuromuscular dynamics of the deadlift, practitioners can improve performance, minimize injury risk, and tailor interventions to the specific needs of athletes. Thus, this review provides a comprehensive overview of the current understanding of deadlift kinematics and EMG activity, offering valuable insights for optimizing training and rehabilitation protocols.

Experimental identification of a linear parametric driver steering control model with torque feedback

Existing mathematical driver models with steering torque feedback are reviewed. It is argued that a new linear parametric driver-steering-vehicle model is required to provide greater insight to subjective assessment of steering torque feedback. The proposed new model incorporates: neuromuscular dynamics; proprioceptive (muscle stretch) and visual sensory dynamics; steering torque perception via efference copy; process and measurement noise; state estimator; and an internal predictive model of the plant. A series of path-following experiments with thirteen test subjects is performed on a fixed-base driving simulator. A Box-Jenkins model accounted for 90% of the variance of the measured handwheel angle time series averaged over the test subjects (VAF). The unknown parameter values of the model were identified from the driving simulator data. The prediction accuracy approached the upper bound given by the Box-Jenkins model. Process noise was found to be linearly dependent on muscle activation torque, and this signal-dependent behaviour was also present in the measurement noise. The model was re-identified using signal to noise ratios as parameters. The VAF was 86.6%, close to that of the Box-Jenkins model, and the identified parameter values are physically plausible, giving confidence that the model is suitable for further work on understanding steering torque feedback.

Neuromuscular State Estimation Via Space-by-Time Neural Signal Decomposition

Abstract Robotic exoskeletons for the hand are being explored to improve health, safety, and physical performance. However, much research effort is needed to establish reliable models of human behavior for effective human–robot interaction control. In this work, surface electromyography is used to measure and model muscle activity of healthy participants performing quasi-isometric and dynamic hand exercises. Non-negative matrix tri-factorization is used to extract hidden neuromuscular parameters encoded in spatial and temporal muscle synergies, which are used to estimate probabilistic linear models of intent, effort, and fatigue. This paper thereby presents steps toward reliable modeling of nonlinear time-varying hand neuromuscular dynamics for intuitive and robust human–robot interaction.

Neuromuscular Synapse Dynamics in Aged and Injured Skeletal Muscle

It is evident that both aged and diseased skeletal muscle exhibit interesting neuromuscular dynamics, characterized by changes in morphology and functionality of the synapse. Although they display a high regenerative capacity, skeletal muscles are still susceptible to nervous system perturbations and impacts of aging. Aged neuromuscular junctions (NMJs) demonstrate a diseased phenotype following some form of muscle atrophy, indicating the presence of paracrine signaling between myofibers and neuronal cells. In this study, we investigated differences in neuromuscular dynamics in injured and aged skeletal muscle using whole‐mount immunostaining and imaging. It has previously been shown that disruption of one component of the muscle microenvironment, such as vasculature, disrupts the structure of NMJs, however it is unclear if aged skeletal muscle experiences similar changes. We hypothesized that both aged and injured NMJs would display similar morphological dynamics, characterized by denervation and fragmentation of postsynaptic acetylcholine receptors (AChRs). We demonstrated that aged and injured NMJs exhibit fragmentation of AChRs, Wallerian degeneration of the presynaptic motor neurons, decreased overlap of pre‐ and post‐synaptic sides, and decreased area of AChR clusters. Due to the prevalence of neuromuscular disorders in the aging population, it is pertinent to understand nerve‐muscle interactions in the healthy aging state in order to hypothesize how these dynamics may be altered in the age‐induced injury states. Ultimately, this will allow us to better comprehend the molecular and cellular mechanisms that are responsible for neuromuscular degeneration in order to develop strategies to improve mobility and quality of life.

Robust Control of the Human Trunk Posture Using Functional Neuromuscular Stimulation: A Simulation Study.

The trunk movements of an individual paralyzed by spinal cord injury (SCI) can be restored by functional neuromuscular stimulation (FNS), which applies low-level current to the motor nerves to activate the paralyzed muscles to generate useful torques, to actuate the trunk. FNS can be modulated to vary the biotorques to drive the trunk to follow a user-defined reference motion and maintain it at a desired postural set-point. However, a stabilizing modulation policy (i.e., control law) is difficult to derive as the biomechanics of the spine and pelvis are complex and the neuromuscular dynamics are highly nonlinear, nonautonomous, and input redundant. Therefore, a control method that can stabilize it with FNS without knowing the accurate skeletal and neuromuscular dynamics is desired. To achieve this goal, we propose a control framework consisting of a robust control module that generates stabilizing torques while an artificial neural network-based mapping mechanism with an anatomy-based updating law ensures that the muscle-generated torques converge to the stabilizing values. For the robust control module, two sliding-mode robust controllers (i.e., a high compensation controller and an adaptive controller), were investigated. System stability of the proposed control method was rigorously analyzed based on the assumption that the skeletal dynamics can be approximated by Euler-Lagrange equations with bounded disturbances, which enables the generalization of the control framework. We present experiments in a simulation environment where an anatomically realistic three-dimensional musculoskeletal model of the human trunk moved in the anterior- posterior and medial-lateral directions while perturbations were applied. The satisfactory simulation results suggest the potential of this control technique for trunk tracking tasks in a typical clinical environment.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi Task Time Series Transformer

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi Task Time Series Transformer

Gait and neuromuscular dynamics during level and uphill walking carrying military loads

ABSTRACT The neuromuscular system responds to perturbation and increasing locomotor task difficulty by altering the stability of neuromuscular output signals. The purpose of this study was to determine the effects of two different military load carriage systems on the dynamic stability of gait and muscle activation signals. 14 army office cadets (20 ± 1 years) performed 4-minute treadmill walking trials on level (0%) and uphill (10%) gradients while unloaded, and with 11 kg backpack and 11 kg webbing loads while the activity of 6 leg and trunk muscles and the motion of the centre of mass (COM) were recorded. Loaded and uphill walking decreased stability and increased magnitude of muscle activations compared to loaded and level gradient walking. Backpack loads increased the medio-lateral stability of COM and uphill walking decreased stability of vertical COM motion and increased stride time variability. However, there was no difference between the two load carriage systems for any variable. The reduced stability of muscle activations in loaded and uphill conditions indicates an impaired ability of the neuromuscular control systems to accommodate perturbations in these conditions which may have implications on the operational performance of military personnel. However, improved medio-lateral stability in backpack conditions may indicate that participants were able to compensate for the loads used in this study, despite the decreased vertical stability and increased stride time variability evident in uphill walking. This study did not find differences between load carriage systems however, specific load carriage system effects may be elicited by greater load carriage masses.

Combinational spectral band activation complexity: Uncovering hidden neuromuscular firing dynamics in EMG

A new approach, Combinational Spectral Band Activation Complexity (CSB-AC), that extracts the neuromuscular firing dynamics of surface electromyography (sEMG) signals by applying entropic methods in a multi-dimensional fashion by analyzing the signals temporally, spectrally, and intensity dynamics simultaneously is presented. The CSB-AC signal processing approach introduces a methodology that highlights that a small amount of key fiducial points embedded within the sEMG, 1000x reduction in EMG data, are only needed to show statistically significant changes of the neuromuscular firing dynamics.CSB-AC was compared to the more generalized sample entropy method to demonstrate physiological differences between cohorts and baseline mapping between the two measurements. Results indicated significant differences between CSB-AC and sample entropy regardless of age groups for tibialis anterior and plantar flexion muscles (gastrocnemius medialis, gastrocnemius lateralis, and soleus). Significant differences were found between older and younger subject groups for the gastrocnemius medialis and soleus with the older adults having higher complexity values. CSB-AC produces greater complexity than sample entropy, where this sparser set of data holds paramount information for describing neuromuscular firing and should not be ignored. CSB-AC, accomplishes this by simultaneously assessing the complexity of sEMG’s time, intensity, and spectral content, where latent properties of neuromuscular dynamics within this unique set of sparse sEMG data points are critical to characterizing neuromuscular firing.

MPC-Based Haptic Shared Steering System: A Driver Modeling Approach for Symbiotic Driving

Advanced driver assistance systems (ADAS) aim to increase safety and reduce mental workload. However, the gap in the understanding of the closed-loop driver–vehicle interaction often leads to reduced user acceptance. In this article, an optimal torque control law is calculated online in the model predictive control (MPC) framework to guarantee continuous guidance during the steering task. The research contribution is in the integration of an extensive prediction model covering cognitive behavior, neuromuscular dynamics, and the vehicle-steering dynamics, within the MPC-based haptic controller to enhance collaboration. The driver model is composed of a preview cognitive strategy based on a linear-quadratic-gaussian, sensory organs, and neuromuscular dynamics, including muscle coactivation and reflex action. Moreover, an adaptive cost-function algorithm enables dynamic allocation of the control authority. Experiments were performed in a fixed-base driving simulator at Toyota Motor Europe involving 19 participants to evaluate the proposed controller with two different cost functions against a commercial lane keeping assist system as an industry benchmark. The results demonstrate the proposed controller fosters symbiotic driving and reduces driver–vehicle conflicts with respect to a state-of-the-art commercial system, both subjectively and objectively, while still improving the path-tracking performance. Summarising, this article tackles the need to blend human and ADAS control, demonstrating the validity of the proposed strategy.

A Reciprocal Excitatory Reflex Between Extensors Reproduces the Prolongation of Stance Phase in Walking Cats: Analysis on a Robotic Platform.

Spinal reflex is essential to the robust locomotion of quadruped animals. To investigate the reflex mechanisms, we developed a quadruped robot platform that emulates the neuromuscular dynamics of animals. The leg is designed to be highly back-drivable, and four Hill-type muscles and neuronal pathways are simulated on each leg using software. By searching for the reflex circuit that contributes to the generation of steady gait in cats through robotic experiments, we found a simple reflex circuit that could produce leg trajectories and a steady gait. In addition, this circuit can reproduce the experimental behavior observed in cats. As a major contribution of this study, we show that the underlying structure of the reflex circuit is the reciprocal coupling between extensor muscles via excitatory neural pathways. In the walking experiments on the robot, a steady gait and experimental behaviors of walking cats emerged from the reflex circuit without any central pattern generators. Furthermore, to take advantage of walking experiments using a neurophysiological robotic platform, we conducted experiments in which a part of the proposed reflex circuit was disconnected for a certain period of time during walking. The results showed that the prolongation of the stance phase caused by the reciprocal excitatory reflex contributed greatly to the generation of a steady gait.

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

The emerging automated driving technology poses a new challenge to driver-automation collaboration, which requires a mutual understanding between humans and machines through their intention identifications. In this article, oriented by human–machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver–vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the bidirectional recurrent neural network and long short-term memory cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.

Neural interfacing architecture enables enhanced motor control and residual limb functionality postamputation

Despite advancements in prosthetic technologies, patients with amputation today suffer great diminution in mobility and quality of life. We have developed a modified below-knee amputation (BKA) procedure that incorporates agonist-antagonist myoneural interfaces (AMIs), which surgically preserve and couple agonist-antagonist muscle pairs for the subtalar and ankle joints. AMIs are designed to restore physiological neuromuscular dynamics, enable bidirectional neural signaling, and offer greater neuroprosthetic controllability compared to traditional amputation techniques. In this prospective, nonrandomized, unmasked study design, 15 subjects with AMI below-knee amputation (AB) were matched with 7 subjects who underwent a traditional below-knee amputation (TB). AB subjects demonstrated significantly greater control of their residual limb musculature, production of more differentiable efferent control signals, and greater precision of movement compared to TB subjects (P < 0.008). This may be due to the presence of greater proprioceptive inputs facilitated by the significantly higher fascicle strains resulting from coordinated muscle excursion in AB subjects (P < 0.05). AB subjects reported significantly greater phantom range of motion postamputation (AB: 12.47 ± 2.41, TB: 10.14 ± 1.45 degrees) when compared to TB subjects (P < 0.05). Furthermore, AB subjects also reported less pain (12.25 ± 5.37) than TB subjects (17.29 ± 10.22) and a significant reduction when compared to their preoperative baseline (P < 0.05). Compared with traditional amputation, the construction of AMIs during amputation confers the benefits of enhanced physiological neuromuscular dynamics, proprioception, and phantom limb perception. Subjects' activation of the AMIs produces more differentiable electromyography (EMG) for myoelectric prosthesis control and demonstrates more positive clinical outcomes.

Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions

SummaryIn this work, pattern recognition and characterization of the neuromuscular dynamics of driver upper limb during naturalistic driving were studied. During the human-in-the-loop experiments, two steering tasks, namely, the passive and active steering tasks, were instructed to be completed by the subjects. Furthermore, subjects manipulated the steering wheel with two distinct postures and six different hand positions. The neuromuscular dynamics of subjects' upper limb were measured using electromyogram signals, and the behavioral data, including the steering torque and steering angle, were also collected. Based on the experimental data, patterns of muscle activities during naturalistic driving were investigated. The correlations, amplitudes, and responsiveness of the electromyogram signals, as well as the smoothness and regularity of the steering torque were discussed. The results reveal the mechanisms of neuromuscular dynamics of driver upper limb and provide a theoretical foundation for the design of the future human-machine interface for automated vehicles.

Bayesian State Estimation in Sensorimotor Systems With Particle Filtering.

In sensorimotor control, sensory feedback integrates with forward models to alleviate the impacts of sensory noise and delay on state estimation. The sensorimotor integration is subject to Bayesian inference and has been formulated by the Kalman filter in computational neuroscience. However, the Kalman filter, as an artificial optimal estimator to address the abstract characteristics of spatial perception, is inadequate to present the neural computation in the cerebellum. Besides, the nonlinear neuromuscular dynamics with tightly coupled state variables also substantially impedes the implementation of Kalman filter in realistic sensorimotor systems. Here we address the sensorimotor state estimate by using the particle filter, a nonlinear Bayesian estimator that can be implemented in arbitrary dynamic systems with the neurocomputational compatibility. Particle filtering is explicitly implemented in a biophysically realistic sensorimotor model of an upper limb integrating Hill-type muscles, tendons, skeleton, and primary afferents. By involving the command noises, the constructed neuromusculoskeletal model qualitatively represents the experimental variability in center-out reaching movements. Despite the initial estimation uncertainty and sensorimotor noises, the particle filter is able to approximate the actual states in forward-reaching movements. Furthermore, the simulated hand-position estimate is consistent with the experimental results, in the presence of forward model errors, neural noises, and sensory delays. The particle filter is demonstrated to effectively implement the Bayesian state estimation in biophysically realistic sensorimotor systems and provide better compatibility with neuronal computation than the Kalman filter.

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

In order to design an advanced human–automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper, a dynamic model of drivers’ neuromuscular interaction with a steering wheel is first established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver–steering–wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss–Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions, and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.

Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model's parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator.

Towards 5G communication systems: Are there health implications?

The spread of radiofrequency electromagnetic fields (RF-EMF) is rising and health effects are still under investigation. RF-EMF promote oxidative stress, a condition involved in cancer onset, in several acute and chronic diseases and in vascular homeostasis. Although some evidences are still controversial, the WHO IARC classified RF-EMF as "possible carcinogenic to humans", and more recent studies suggested reproductive, metabolic and neurologic effects of RF-EMF, which are also able to alter bacterial antibiotic resistance. In this evolving scenario, although the biological effects of 5G communication systems are very scarcely investigated, an international action plan for the development of 5G networks has started, with a forthcoming increment in devices and density of small cells, and with the future use of millimeter waves (MMW). Preliminary observations showed that MMW increase skin temperature, alter gene expression, promote cellular proliferation and synthesis of proteins linked with oxidative stress, inflammatory and metabolic processes, could generate ocular damages, affect neuro-muscular dynamics. Further studies are needed to better and independently explore the health effects of RF-EMF in general and of MMW in particular. However, available findings seem sufficient to demonstrate the existence of biomedical effects, to invoke the precautionary principle, to define exposed subjects as potentially vulnerable and to revise existing limits. An adequate knowledge of pathophysiological mechanisms linking RF-EMF exposure to health risk should also be useful in the current clinical practice, in particular in consideration of evidences pointing to extrinsic factors as heavy contributors to cancer risk and to the progressive epidemiological growth of noncommunicable diseases.

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model.

Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem (OCP) is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution toward bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

Biodynamic Feedthrough: Current Status and Open Issues

Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the body of a human operator, causing involuntary limb motions, which in turn result in involuntary control inputs. Manual control of many different vehicles is known to be vulnerable to BDFT effects, such as that of helicopters, aircraft, electric wheelchairs and hydraulic excavators. This paper provides a brief review of BDFT literature, which serves as a basis for identifying the fundamental challenges that remain to be addressed in future BDFT research. One of these challenges, time-variant BDFT identification, is discussed in more detail. Currently, it is often assumed that BDFT dynamics are (quasi)linear and time-invariant. This assumption can only be justified when measuring BDFT under carefully crafted experimental conditions, which are very different from real-world situations. As BDFT dynamics depend on neuromuscular dynamics, they are typically time-varying. This paper investigates the suitability of a recently developed time-variant identification approach, based on a recursive least-squares algorithm, which has been successfully used to identify time-varying neuromuscular dynamics.

Human Motor Control and the Internal Model Principle

Abstract: In the motor behavior literature, the observation that humans can learn and adapt to altered limb dynamics is often explained with reference to internal models. Forward internal models enable robust performance even when feedback is intermittent or delayed while inverse models simplify the loop dynamics to manageable form. Internal models of coupled limb/object dynamics, however, do not address the skill with which humans track references or reject disturbances that can be recognized and anticipated. We propose that internal models of reference or disturbance signals create a useful framework for addressing open questions in human motor behavior. We appeal to the Internal Model Principle in control theory, which suggests that a model of the predictable signal (a signal generator) belongs in the controller. When the control loop is already stable, such an internal model will produce perfect tracking or disturbance rejection in steady state. In this paper we apply the Internal Model Principle to hypothesize controllers that elicit cyclic behaviors from systems that feature elastic and inertial energy storage. We also outfit this model with features that describe human neuromuscular dynamics and thereby enable the description of two roles for force feedback: carrying power that couples the human/machine dynamics and carrying information for neural control processing.