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In this research article, a control approach for port-Hamiltonian PH systems based in a neural network (NN) quaternion-based control strategy is presented. First, the dynamics is converted by the implementation of a Poisson bracket in order to facilitate the mathematical model in order to obtain a feasible formulation for the controller design based on quaternion NNs. In this study, two controllers for this kind of of system are presented: the first one consists in the controller design for a PH system about its equilibrium points taking into consideration the position and momentum. This mean is achieved by dividing the quaternion neural controller into scalar and vectorial parts to facilitate the controller derivation by selecting a Lyapunov functional. The second control strategy consists in designing the trajectory tracking controller, in which a reference moment is considered in order to drive this variable to the final desired position according to a reference variable; again, a Lyapunov functional is implemented to obtain the desired control law. It is important to mention that both controllers take into advantage that the energy consideration and that the representation of many physical systems could be implemented in quaternions. Besides the angular velocity, trajectory tracking of a three-phase induction motor is presented as a third numerical experiment. Two numerical experiments are presented to validate the theoretical results evinced in this study. Finally, a discussion and conclusion section is provided.

The human's upright standing is a complex control process that is not yet fully understood. Postural controlmodels can provide insights into the body's internal control processes of balance behavior. Using physiologically plausible models can also help explaining pathophysiological motion behavior. In this paper, we introduce a neuromusculoskeletal postural controlmodel using sensor feedback consisting of somatosensory, vestibular and visual information. The sagittal plane model was restricted to effectively six degrees of freedom and consisted of nine muscles per leg. Physiologically plausible neural delays were considered for balance control. We applied forward dynamic simulations and a single shooting approach to generate healthy reactive balance behavior during quiet and perturbed upright standing. Control parameters were optimized tominimize muscle effort. We showed that our model is capable of fulfilling the applied tasks successfully. We observed joint angles and ranges of motion in physiologically plausible ranges and comparable to experimental data. This model represents the starting point for subsequent simulations of pathophysiological postural control behavior. [ABSTRACT FROM AUTHOR]

Ankle joint stiffness and viscosity are fundamental mechanical descriptions that govern the movement of the body and impact an individual's walking ability. Hence, these internal properties of a joint have been increasingly used to evaluate the effects of pathology (e.g., stroke) and in the design and control of robotic and prosthetic devices. However, the reliability of these measurements is currently unclear, which is important for translation to clinical use. Can we reliably measure the mechanical impedance parameters of the ankle while standing and walking? Eighteen able-bodied individuals volunteered to be tested on two different days separated by at least 24 h. Participants received several small random ankle dorsiflexion perturbations while standing and during the stance phase of walking using a custom-designed robotic platform. Three-dimensional motion capture cameras and a 6-component force plate were used to quantify ankle joint motions and torque responses during normal and perturbed conditions. Ankle mechanical impedance was quantified by computing participant-specific ensemble averages of changes in ankle angle and torque due to perturbation and fitting a second-order parametric model consisting of stiffness, viscosity, and inertia. The test-retest reliability of each parameter was assessed using intraclass correlation coefficients (ICCs). We also computed the minimal detectable change (MDC) for each impedance parameter to establish the smallest amount of change that falls outside the measurement error of the instrument. In standing, the reliability of stiffness, viscosity, and inertia was good to excellent (ICCs=0.67–0.91). During walking, the reliability of stiffness and viscosity was good to excellent (ICCs=0.74–0.84) while that of inertia was fair to good (ICCs=0.47–0.68). The MDC for a single subject ranged from 20%− 65% of the measurement mean but was higher (>100%) for inertia during walking. Results indicate that dynamic measures of ankle joint impedance were generally reliable and could serve as an adjunct clinical tool for evaluating gait impairments. • The ankle impedance provides insight into the internal mechanisms that govern gait. • The test-retest reliability of ankle impedance was tested in standing and walking. • In standing, reliability of stiffness, viscosity, and inertia was good to excellent. • During walking, the reliability of stiffness and viscosity was good to excellent. • Dynamic measures of ankle joint impedance were higher during standing than walking. [ABSTRACT FROM AUTHOR]

The human's upright standing is a complex control process that is not yet fully understood. Postural control models can provide insights into the body's internal control processes of balance behavior. Using physiologically plausible models can also help explaining pathophysiological motion behavior. In this paper, we introduce a neuromusculoskeletal postural control model using sensor feedback consisting of somatosensory, vestibular and visual information. The sagittal plane model was restricted to effectively six degrees of freedom and consisted of nine muscles per leg. Physiologically plausible neural delays were considered for balance control. We applied forward dynamic simulations and a single shooting approach to generate healthy reactive balance behavior during quiet and perturbed upright standing. Control parameters were optimized to minimize muscle effort. We showed that our model is capable of fulfilling the applied tasks successfully. We observed joint angles and ranges of motion in physiologically plausible ranges and comparable to experimental data. This model represents the starting point for subsequent simulations of pathophysiological postural control behavior.

There is great interest in the development of prosthetic limbs capable of complex activities that are wirelessly connected to the patient's neural system. Although some progress has been achieved in this area, one of the main problems encountered is the selective acquisition of nerve impulses and the closing of the automation loop through the selective stimulation of the sensitive branches of the patient. Large-scale research and development have achieved so-called "cuff electrodes"; however, they present a big disadvantage: they are not selective. In this article, we present the progress made in the development of an implantable system of plug neural microelectrodes that relate to the biological nerve tissue and can be used for the selective acquisition of neuronal signals and for the stimulation of specific nerve fascicles. The developed plug electrodes are also advantageous due to their small thickness, as they do not trigger nerve inflammation. In addition, the results of the conducted tests on a sous scrofa subject are presented. [ABSTRACT FROM AUTHOR]

This work presents the fabrication and characterization of a triple-layered biomimetic muscle constituted by polypyrrole (PPy)-dodecylbenzenesulfonate (DBS)/adhesive tape/PPy-DBS demonstrating simultaneous sensing and actuation capabilities. The muscle was controlled by a neurobiologically inspired cortical neural network sending agonist and antagonist signals to the conducting polymeric layers. Experiments consisted of controlled voluntary movements of the free end of the muscle at angles of ±20°, ±30°, and ±40° while monitoring the muscle's potential response. Results show the muscle's potential varies linearly with applied current amplitude during actuation, enabling current sensing. A linear dependence between muscle potential and temperature enabled temperature sensing. Electrolyte concentration changes also induced exponential variations in the muscle's potential, allowing for concentration sensing. Additionally, the influence of the electric current density on the angular velocity, the electric charge density, and the desired angle was studied. Overall, the conducting polymer-based soft biomimetic muscle replicates properties of natural muscles, permitting simultaneous motion control, current, temperature, and concentration sensing. The integrated neural control system exhibits key features of biological motion regulation. This muscle actuator with its integrated sensing and control represents an advance for soft robotics, prosthetics, and biomedical devices requiring biomimetic multifunctionality. [ABSTRACT FROM AUTHOR]

Abstract Time‐varying matrix inversion is an important field of matrix research, and lots of research achievements have been obtained. In the process of solving time‐varying matrix inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while solving the problem is required. In this paper, an advanced continuous‐time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time‐varying matrix inversion, which has incomparable disturbance‐suppression property. For digital hardware applications, the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression.

Legged robots that can instantly change motor patterns at different walking speeds are useful and can accomplish various tasks efficiently. However, state-of-the-art control methods either are difficult to develop or require long training times. In this study, we present a comprehensible neural control framework to integrate probability-based black-box optimization (PI BB) and supervised learning for robot motor pattern generation at various walking speeds. The control framework structure is based on a combination of a central pattern generator (CPG), a radial basis function (RBF) -based premotor network and a hypernetwork, resulting in a so-called neural CPG-RBF-hyper control network. First, the CPG-driven RBF network, acting as a complex motor pattern generator, was trained to learn policies (multiple motor patterns) for different speeds using PI BB. We also introduce an incremental learning strategy to avoid local optima. Second, the hypernetwork, which acts as a task/behavior to control parameter mapping, was trained using supervised learning. It creates a mapping between the internal CPG frequency (reflecting the walking speed) and motor behavior. This map represents the prior knowledge of the robot, which contains the optimal motor joint patterns at various CPG frequencies. Finally, when a user-defined robot walking frequency or speed is provided, the hypernetwork generates the corresponding policy for the CPG-RBF network. The result is a versatile locomotion controller which enables a quadruped robot to perform stable and robust walking at different speeds without sensory feedback. The policy of the controller was trained in the simulation (less than 1 h) and capable of transferring to a real robot. The generalization ability of the controller was demonstrated by testing the CPG frequencies that were not encountered during training. [ABSTRACT FROM AUTHOR]

Time‐varying matrix inversion is an important field of matrix research, and lots of research achievements have been obtained. In the process of solving time‐varying matrix inversion, disturbances inevitably exist, thus, a model that can suppress disturbance while solving the problem is required. In this paper, an advanced continuous‐time recurrent neural network (RNN) model based on a double integral RNN design formula is proposed for solving continuous time‐varying matrix inversion, which has incomparable disturbance‐suppression property. For digital hardware applications, the corresponding advanced discrete‐time RNN model is proposed based on the discretisation formulas. As a result of theoretical analysis, it is demonstrated that the advanced continuous‐time RNN model and the corresponding advanced discrete‐time RNN model have global and exponential convergence performance, and they are excellent for suppressing different disturbances. Finally, inspiring experiments, including two numerical experiments and a practical experiment, are presented to demonstrate the effectiveness and superiority of the advanced discrete‐time RNN model for solving discrete time‐varying matrix inversion with disturbance‐suppression. [ABSTRACT FROM AUTHOR]

Living animals and legged robots share similar challenges for movement control. In particular, the investigation of neural control mechanisms for the self‐organized locomotion of insects and hexapod robots can be informative for other fields. The Annam stick insect Medauroidea extradentata is used as a template to develop a biorobotic model to infer walking self‐organization with strongly heterogeneous leg lengths. Body dimensions and data on the walking dynamics of the actual stick insect are used for the development of a neural control mechanism, generating self‐organized gait patterns that correspond to the real insect observations. The combination of both investigations not only proposes solutions for distributed neural locomotion control but also enables insights into the neural equipment of the biological template. Decentralized neural central pattern generation is utilized with phase modulation based on foot contact feedback to generate adaptive periodic base patterns and a radial basis function premotor network in each leg based on the target trajectories of actual stick insect legs during walking for complex intralimb coordination and self‐organized interlimb coordination control. Furthermore, based on both study objects, a robot with heterogeneous leg lengths is constructed to preliminary validate the findings from the simulations and real insect observations. [ABSTRACT FROM AUTHOR]

Neuroplasticity after anterior cruciate ligament (ACL) injury alters how the nervous system generates movement and maintains dynamic joint stability. The postinjury neuroplasticity can cause neural compensations that increase reliance on neurocognition. Return-to-sport testing quantifies physical function but fails to detect important neural compensations. To assess for neural compensations in a clinical setting, we recommend evaluating athletes' neurocognitive reliance by augmenting return-to-sport testing with combined neurocognitive and motor dual-task challenges. In this Viewpoint, we (1) share the latest evidence related to ACL injury neuroplasticity and (2) share simple principles and new assessments with preliminary data to improve return-to-sport decisions following ACL reconstruction. [ABSTRACT FROM AUTHOR]

High blood pressure, also known as hypertension, is a major risk factor for cardiovascular disease. Salt intake has been shown to have a significant impact on BP, but the mechanisms by which it influences the blood pressure dipping pattern, and 24-h blood pressure remains controversial. This literature review aims to both summarize the current evidence on high salt diet induced hypertension and discuss the epidemiological aspects including socioeconomic issues in the United States and abroad. Our review indicates that a high salt diet is associated with a blunted nocturnal blood pressure dipping pattern, which is characterized by a reduced decrease in blood pressure during the nighttime hours. The mechanisms by which high salt intake affects blood pressure dipping patterns are not fully understood, but it is suggested that it may be related to changes in the sympathetic nervous system. Further, we looked at the association between major blood pressure and circadian rhythm regulatory centers in the brain, including the paraventricular nucleus (PVN), suprachiasmatic nucleus (SCN) and nucleus tractus solitarius (nTS). We also discuss the underlying social and economic issues in the United States and around the world. In conclusion, the evidence suggests that a high salt diet is associated with a blunted, non-dipping, or reverse dipping blood pressure pattern, which has been shown to increase the risk of cardiovascular disease. Further research is needed to better understand the underlying mechanisms by which high salt intake influences changes within the central nervous system. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) can be used to study activity patterns in different brain cortical areas involved in balance control. This systematic review aims to report on studies in which balance performance has been quantitatively assessed concurrent with fNIRS neuroimaging. Following the PRISMA guidelines, relevant keywords were used for the search through the Scopus and Web of Science databases. Sixty-eight studies met the inclusion criteria and were imported for data extraction. Information on balance assessment protocols, alterations to the balance control loop, brain regions of interest, fNIRS parameters, the relationships found between brain activity and balance performance, and participant cohort types was extracted. The common balance tasks in fNIRS studies were standing and walking. Standing balance control was mainly measured through sway parameters using force platforms. Walking performance was evaluated through gait parameters mostly assessed by floor sensors or inertial sensors. Some of the balance tasks were challenged through sensory manipulation or dual task interference. Brain activity monitoring via fNIRS was mainly utilized to measure oxygenated haemoglobin concentration in frontal cortex. Out of the 68 included articles, 22 studies investigated and found the relationships between activity patterns in different cortical areas and balance measures. In 32 studies, the effects of different factors such as long-term, biological, and psychological conditions on brain activity and balance performance were studied. This study provides a systematic review on fNIRS studies in which quantitative balance assessment is employed to provide a better understanding of neuromotor control of balance.

Abstract Based on the predigestion of the dynamic model of the intelligent firefighting vehicle, a linear 2‐DOF lateral dynamic model and a preview error model are established. To solve the problems of a highly non‐linear vehicle model, time‐varying parameters, output chattering, and poor robustness, the Radial Basis Function neural network sliding mode controller is designed. Then, different driving speeds are used to conduct simulation tests under standard double‐shifting and smooth curve road conditions, and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path. The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Introduction: Animals such as cattle can achieve versatile and elegant behaviors through automatic sensorimotor coordination. Their self-organized movements convey an impression of adaptability, robustness, and motor memory. However, the adaptive mechanisms underlying such natural abilities of these animals have not been completely realized in artificial legged systems. Methods: Hence, we propose adaptive neural control that can mimic these abilities through adaptive physical and neural communications. The control algorithm consists of distributed local central pattern generator (CPG)-based neural circuits for generating basic leg movements, an adaptive sensory feedback mechanism for generating self-organized phase relationships among the local CPG circuits, and an adaptive neural coupling mechanism for transferring and storing the formed phase relationships (a gait pattern) into the neural structure. The adaptive neural control was evaluated in experiments using a quadruped robot. Results: The adaptive neural control enabled the robot to 1) rapidly and automatically form its gait (i.e., self-organized locomotion) within a few seconds, 2) memorize the gait for later recovery, and 3) robustly walk, even when a sensory feedback malfunction occurs. It also enabled maneuverability, with the robot being able to change its walking speed and direction. Moreover, implementing adaptive physical and neural communications provided an opportunity for understanding the mechanism of motor memory formation. Discussion: Overall, this study demonstrates that the integration of the two forms of communications through adaptive neural control is a powerful way to achieve robust and reusable self-organized locomotion in legged robots. [ABSTRACT FROM AUTHOR]

High blood pressure, also known as hypertension, is a major risk factor for cardiovascular disease. Salt intake has been shown to have a significant impact on BP, but the mechanisms by which it influences the blood pressure dipping pattern, and 24-h blood pressure remains controversial. This literature review aims to both summarize the current evidence on high salt diet induced hypertension and discuss the epidemiological aspects including socioeconomic issues in the United States and abroad. Our review indicates that a high salt diet is associated with a blunted nocturnal blood pressure dipping pattern, which is characterized by a reduced decrease in blood pressure during the nighttime hours. The mechanisms by which high salt intake affects blood pressure dipping patterns are not fully understood, but it is suggested that it may be related to changes in the sympathetic nervous system. Further, we looked at the association between major blood pressure and circadian rhythm regulatory centers in the brain, including the paraventricular nucleus (PVN), suprachiasmatic nucleus (SCN) and nucleus tractus solitarius (nTS). We also discuss the underlying social and economic issues in the United States and around the world. In conclusion, the evidence suggests that a high salt diet is associated with a blunted, non-dipping, or reverse dipping blood pressure pattern, which has been shown to increase the risk of cardiovascular disease. Further research is needed to better understand the underlying mechanisms by which high salt intake influences changes within the central nervous system.

There is great interest in the development of prosthetic limbs capable of complex activities that are wirelessly connected to the patient’s neural system. Although some progress has been achieved in this area, one of the main problems encountered is the selective acquisition of nerve impulses and the closing of the automation loop through the selective stimulation of the sensitive branches of the patient. Large-scale research and development have achieved so-called “cuff electrodes”; however, they present a big disadvantage: they are not selective. In this article, we present the progress made in the development of an implantable system of plug neural microelectrodes that relate to the biological nerve tissue and can be used for the selective acquisition of neuronal signals and for the stimulation of specific nerve fascicles. The developed plug electrodes are also advantageous due to their small thickness, as they do not trigger nerve inflammation. In addition, the results of the conducted tests on a sous scrofa subject are presented.

This work presents the fabrication and characterization of a triple-layered biomimetic muscle constituted by polypyrrole (PPy)-dodecylbenzenesulfonate (DBS)/adhesive tape/PPy-DBS demonstrating simultaneous sensing and actuation capabilities. The muscle was controlled by a neurobiologically inspired cortical neural network sending agonist and antagonist signals to the conducting polymeric layers. Experiments consisted of controlled voluntary movements of the free end of the muscle at angles of ±20°, ±30°, and ±40° while monitoring the muscle’s potential response. Results show the muscle’s potential varies linearly with applied current amplitude during actuation, enabling current sensing. A linear dependence between muscle potential and temperature enabled temperature sensing. Electrolyte concentration changes also induced exponential variations in the muscle’s potential, allowing for concentration sensing. Additionally, the influence of the electric current density on the angular velocity, the electric charge density, and the desired angle was studied. Overall, the conducting polymer-based soft biomimetic muscle replicates properties of natural muscles, permitting simultaneous motion control, current, temperature, and concentration sensing. The integrated neural control system exhibits key features of biological motion regulation. This muscle actuator with its integrated sensing and control represents an advance for soft robotics, prosthetics, and biomedical devices requiring biomimetic multifunctionality.

Humans make a number of choices when they walk, such as how fast and for how long. The preferred steady walking speed seems chosen to minimize energy expenditure per distance traveled. But the speed of actual walking bouts is not only steady, but rather a time-varying trajectory, which can also be modulated by task urgency or an individual’s movement vigor. Here we show that speed trajectories and durations of human walking bouts are explained better by an objective to minimize Energy and Time, meaning the total work or energy to reach destination, plus a cost proportional to bout duration. Applied to a computational model of walking dynamics, this objective predicts dynamic speed vs. time trajectories with inverted U shapes. Model and human experiment (N=10) show that shorter bouts are unsteady and dominated by the time and effort of accelerating, and longer ones are steadier and faster and dominated by steady-state time and effort. Individual-dependent vigor may be characterized by the energy one is willing to spend to save a unit of time, which explains why some may walk faster than others, but everyone may have similar-shaped trajectories due to similar walking dynamics. Tradeoffs between energy and time costs can predict transient, steady, and vigor-related aspects of walking.

Bayesian filters have been considered to help refine and develop theoretical views on spatial cell functions for self-localization. However, extending a Bayesian filter to reproduce insect-like navigation behaviors (e.g., home searching) remains an open and challenging problem. To address this problem, we propose an embodied neural controller for self-localization, foraging, backward homing (BH), and home searching of an advanced mobility sensor (AMOS)-driven insect-like robot. The controller, comprising a navigation module for the Bayesian self-localization and goal-directed control of AMOS and a locomotion module for coordinating the 18 joints of AMOS, leads to its robust insect-like navigation behaviors. As a result, the proposed controller enables AMOS to perform robust foraging, BH, and home searching against various levels of sensory noise, compared to conventional controllers. Its implementation relies only on self-localization and heading perception, rather than global positioning and landmark guidance. Interestingly, the proposed controller makes AMOS achieve spiral searching patterns comparable to those performed by real insects. We also demonstrated the performance of the controller for real-time indoor and outdoor navigation in a real insect-like robot without any landmark and cognitive map. [ABSTRACT FROM AUTHOR]

Metabolic syndrome encompasses a spectrum of conditions that increases the risk for cardiovascular and metabolic diseases. It is widely accepted that the sex hormone estrogen plays a protective metabolic role in premenopausal women, in part through central nervous system (CNS) mechanisms. However, most work to date has focused on the loss of estrogen in females (e.g., menopause). Interestingly, transgender individuals receiving feminizing gender affirming therapy (i.e., estrogen) are relatively protected from metabolic syndrome conditions, pointing to a role for CNS estrogen in the development of metabolic syndrome in men. Here, we show that estrogen signaling in the brain protects males from metabolic syndrome and obesity related complications. First, short-term CNS specific supplementation of low-dose 17-β-estradiol in diet-induced obese male mice resulted in a significant reduction in body weight in parallel with a decrease in food intake without alterations in energy expenditure. In conjunction, central supplementation of estrogen reduced visceral adiposity, including epididymal and abdominal regions, with slighter decreases in subcutaneous inguinal and thermogenic brown adipose tissue. Furthermore, central estrogen administration reduced the liver manifestation of metabolic syndrome including hepatomegaly and hepatic steatosis. Collectively, these findings indicate that a lack of estrogen action in the brain may predispose males to metabolic syndrome pathogenesis. [ABSTRACT FROM AUTHOR]

A novel adaptive control scheme is developed for active suspension systems (ASSs) based on neural networks (NNs) and backstepping control strategies. Since the springs and piecewise dampers are nonlinear, the unknown internal dynamics are approximated by radial basis function neural networks (RBFNNs). Then, to solve the time-varying constrains of both vertical displacement and corresponding speed in vehicle body, the Tangent Barrier Lyapunov Functions (TBLFs) are incorporated into the controller design. Furthermore, the adaptive controller and adaptive laws are designed to improve the riding comfortable, handling stability and driving safety. In the end, the simulation results show the effectiveness and feasibility of the proposed adaptive algorithm compared with unconstrained adaptive approach. [ABSTRACT FROM AUTHOR]

A recent line of work suggests that the net behavior of the foot-ground interaction force provides insight into quiet-standing-balance dynamics and control. Through human subject experiments, Boehm et al. found that the relative variations of the center of pressure and force orientation emerge as a distinct pattern in the frequency domain, termed the "intersection-point height." Subsequent empirical and simulation-based studies showed that different control strategies are reflected in the distribution of intersection-point height across frequency. To facilitate understanding of the strengths and limitations of the intersection-point height in describing the dynamics and control of standing, the present work establishes a spectral-based method that also enables derivation of a closed-form estimate of the intersection-point height from any linear model of quiet stance. This new method explained observations from prior work, including how the measure captures aspects of control and physiological noise. The analysis presented herein highlights the utility of the frequency-dependent foot-force dynamics in probing the balance controller and provides a tool for model development and validation to further our understanding of the neuromotor control of natural upright posture in humans.

Aging can cause the decline of balance ability, which can lead to increased falls and decreased mobility. This work aimed to discern differences in balance control between healthy older and younger adults. Foot force data of 38 older and 65 younger participants (older and younger than 60 years, respectively) were analyzed. To first determine whether the two groups exhibited any differences, this study incorporated the orientation of the foot-ground interaction force in addition to its point of application. Specifically, the frequency-dependence of the "intersection point" of the lines of actions of the foot-ground interaction forces were evaluated. Results demonstrated that, like the mean center-of-pressure speed, a traditionally-employed measure, the intersection-point analysis could distinguish between the two participant groups. Then, to further explore age-specific control strategies, simulations of standing balance were conducted. An optimal controller stabilized a double-inverted-pendulum model with torque-actuated ankle and hip joints corrupted with white noise. The experimental data were compared to the simulation results to identify the controller parameters that best described the human data. Older participants showed significantly more use of the ankle than hip compared to younger participants. Best-fit controller gains suggested increased preference for asymmetric inter-joint neural feedback, possibly to compensate for the effects of aging such as sarcopenia. These results underscore the advantages of the intersection-point analysis to quantify possible shifts in inter-joint control with age, thus highlighting its potential to be used as a balance assessment tool in research and clinical settings.

Many challenging problems that consider the analysis and control of neural brain rhythms have been motivated by the advent of deep brain stimulation as a therapeutic treatment for a wide variety of neurological disorders. In a computational setting, neural rhythms are often modeled using large populations of coupled, conductance-based neurons. Control of such models comes with a long list of challenges: the underlying dynamics are nonnegligibly nonlinear, high dimensional, and subject to noise; hardware and biological limitations place restrictive constraints on allowable inputs; direct measurement of system observables is generally limited; and the resulting systems are typically highly underactuated. In this review article, we highlight a collection of recent analysis techniques and control frameworks that have been developed to contend with these difficulties. Particular emphasis is placed on the problem of desynchronization for a population of pathologically synchronized neural oscillators, a problem that is motivated by applications to Parkinson's disease where pathological synchronization is thought to contribute to the associated motor control symptoms. We also discuss other recent neural control applications that consider entrainment, phase randomization, synchronization, and clustering. [ABSTRACT FROM AUTHOR]

The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk). These body parts can exhibit specific coordination patterns with each other. In human walking, we found that the swing leg cooperates with the upper body and the stance leg in different ways (e.g., in-phase and out-of-phase in vertical and horizontal directions, respectively). Such patterns of self-assistance found in human locomotion could be of advantage in robotics design, in the design of any assistive device for patients with movement impairments. It can also shed light on several unexplained infrastructural features of the CNS motor control. Self-assistance means that distributed parts of the body contribute to an overlay of functions that are required to solve the underlying motor task. To draw advantage of self-assisting effects, precise and balanced spatiotemporal patterns of muscle activation are necessary. We show that the necessary neural connectivity infrastructure to achieve such muscle control exists in abundance in the spinocerebellar circuitry. We discuss how these connectivity patterns of the spinal interneurons appear to be present already perinatally but also likely are learned. We also discuss the importance of these insights into whole body locomotion for the successful design of future assistive devices and the sense of control that they could ideally confer to the user. [ABSTRACT FROM AUTHOR]

The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk). These body parts can exhibit specific coordination patterns with each other. In human walking, we found that the swing leg cooperates with the upper body and the stance leg in different ways (e.g., in-phase and out-of-phase in vertical and horizontal directions, respectively). Such patterns of self-assistance found in human locomotion could be of advantage in robotics design, in the design of any assistive device for patients with movement impairments. It can also shed light on several unexplained infrastructural features of the CNS motor control. Self-assistance means that distributed parts of the body contribute to an overlay of functions that are required to solve the underlying motor task. To draw advantage of self-assisting effects, precise and balanced spatiotemporal patterns of muscle activation are necessary. We show that the necessary neural connectivity infrastructure to achieve such muscle control exists in abundance in the spinocerebellar circuitry. We discuss how these connectivity patterns of the spinal interneurons appear to be present already perinatally but also likely are learned. We also discuss the importance of these insights into whole body locomotion for the successful design of future assistive devices and the sense of control that they could ideally confer to the user.

Under simultaneous ambient temperature and postural stressors, integrated regional blood flow responses are required to maintain blood pressure and thermoregulatory homeostasis. The aim of the present study was to assess the effect of ambient temperature and body posture on regional regulation of microvascular blood flow, specifically in the arms and legs. Participants (N = 11) attended two sessions in which they experienced transient ambient conditions, in a climatic chamber. During each 60-min trial, ambient temperature increased from 15.7 (0.6) °C to 38.9 (0.6) °C followed by a linear decrease, and the participants were either standing or in a supine position throughout the trial; relative humidity in the chamber was maintained at 25.9 (6.6) %. Laser doppler flowmetry of the forearm (SkBF arm) and calf (SkBF calf), and haemodynamic responses (heart rate, HR; stroke volume, SV; cardiac output, CO; blood pressure, BP), were measured continuously. Analyses of heart rate variability and wavelet transform were also conducted. SkBF arm increased significantly at higher ambient temperatures (p = 0.003), but not SkBF calf. The standing posture caused lower overall SkBF in both regions throughout the protocol, regardless of temperature (p < 0.001). HR and BP were significantly elevated, and SV significantly lowered, in response to separate and combined effects of higher ambient temperatures and a standing position (all p < 0.05); CO remained unchanged. Mechanistic analyses identified greater sympathetic nerve activation, and higher calf myogenic activation at peak temperatures, in the standing condition. Mechanistically and functionally, arm vasculature responds to modulation from both thermoregulation and baroreceptor activity. The legs, meanwhile, are more sensitive to baroreflex regulatory mechanisms. • Exposure to combined thermal and postural stressors requires interaction of thermoregulatory and baroreflex responses. • Regional variation in control from thermoregulatory and baroreflex mechanisms may exist in the maintenance of blood flow. • Participants experienced a transient ambient temperature (15.7(0.6) °C - 38.9(0.6)°C), in two seperate postures (upright and supine). • Microvascular blood flow and haemodynamic responses were recorded continuously throughout the protocol. • Arm blood flow increased during the rise in ambient temperature; however both limbs decreased blood flow during standing. • Arm vasculature seems to be regulated by thermoregulatory and baroreflex mechanisms, while the legs are regulated by the baroreflex alone. [ABSTRACT FROM AUTHOR]

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons—that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the desired behavioral state of the animal. Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in state-dependent effects. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments. [ABSTRACT FROM AUTHOR]

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Behaviors we perform each day, such as manipulating an object or walking, require precise control of the interaction forces between our bodies and the environment. These forces are generated by muscle contractions, specified by the nervous system, and by joint mechanics, determined by the intrinsic properties of the musculoskeletal system. Depending on behavioral goals, joint mechanics might simplify or complicate control of movement by the nervous system. Whether humans can exploit joint mechanics to simplify neural control remains unclear. Here we evaluated if leveraging joint mechanics simplifies neural control by comparing performance in three tasks that required subjects to generate specified torques about the ankle during imposed sinusoidal movements; only one task required torques that could be generated by leveraging the intrinsic mechanics of the joint. The complexity of the neural control was assessed by subjects' perceived difficulty and the resultant task performance. We developed a novel approach that used continuous estimates of ankle impedance, a quantitative description of the joint mechanics, and measures of muscle activity to determine the mechanical and neural contributions to the net ankle torque generated in each task. We found that the torque resulting from changes in neural control was reduced when ankle impedance was consistent with the task being performed. Subjects perceived this task to be easier than those that were not consistent with the impedance of the ankle and were able to perform it with the highest level of consistency across repeated trials. These results demonstrate that leveraging the mechanical properties of a joint can simplify task completion and improve performance. [ABSTRACT FROM AUTHOR]

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics. [ABSTRACT FROM AUTHOR]

Objective: Ankle joint stiffness is known to be modulated by co-contraction of the ankle muscles; however, it is unclear to what extent changes in agonist muscle activation alone affect ankle joint stiffness. This study tested the effects of varying levels of ankle muscle activation on ankle joint mechanical stiffness in standing and during the late stance phase of walking. Methods: Dorsiflexion perturbations were applied at various levels of ankle muscle activation via a robotic platform in standing and walking conditions. In standing, muscle activation was modulated by having participants perform an EMG target matching task that required varying levels of plantarflexor activation. In walking, muscle activation was modulated by changing walking speeds through metronome-based auditory feedback. Ankle stiffness was evaluated by performing a Least-squares system identification using a parametric model consisting of stiffness, damping, and inertia. The association between ankle muscle activation and joint stiffness was evaluated using correlation analyses. Linear regression models were used to determine the extent to which muscle activation contributed to ankle stiffness. An inclusive statistical approach (both classical and Bayesian analyses) was adopted to measure the statistical significance (p-value) and Bayes Factor (BF10). Results: Results indicate that plantarflexor activity was positively correlated with ankle stiffness in both standing and walking (p<0.001, BF10900), whereas dorsiflexor activity was negatively correlated with ankle stiffness in walking (p = 0.014, BF10 = 3.9) but not in standing (p = 0.725). Regression analyses indicated that ankle muscle activation predicted about 84% of the variation in ankle stiffness in standing and 45% in walking (p<0.001, BF10100). Conclusion: Ankle muscle activation significantly contributes to ankle stiffness during standing and walking. Significance: The results highlight the role of muscle activation on maintaining joint stiffness and underscore the importance of accounting for muscle activation when measuring ankle stiffness in healthy as well as patient populations. [ABSTRACT FROM AUTHOR]

In almost all animals, the transfer of information from the brain to the motor circuitry is facilitated by a relatively small number of neurons, leading to a constraint on the amount of information that can be transmitted. Our knowledge of how animals encode information through this pathway, and the consequences of this encoding, however, is limited. In this study, we use a simple feed-forward neural network to investigate the consequences of having such a bottleneck and identify aspects of the network architecture that enable robust information transfer. We are able to explain some recently observed properties of descending neurons—that they exhibit a modular pattern of connectivity and that their excitation leads to consistent alterations in behavior that are often dependent upon the desired behavioral state of the animal. Our model predicts that in the presence of an information bottleneck, such a modular structure is needed to increase the efficiency of the network and to make it more robust to perturbations. However, it does so at the cost of an increase in state-dependent effects. Despite its simplicity, our model is able to provide intuition for the trade-offs faced by the nervous system in the presence of an information processing constraint and makes predictions for future experiments.

Behaviors we perform each day, such as manipulating an object or walking, require precise control of the interaction forces between our bodies and the environment. These forces are generated by muscle contractions, specified by the nervous system, and by joint mechanics, determined by the intrinsic properties of the musculoskeletal system. Depending on behavioral goals, joint mechanics might simplify or complicate control of movement by the nervous system. Whether humans can exploit joint mechanics to simplify neural control remains unclear. Here we evaluated if leveraging joint mechanics simplifies neural control by comparing performance in three tasks that required subjects to generate specified torques about the ankle during imposed sinusoidal movements; only one task required torques that could be generated by leveraging the intrinsic mechanics of the joint. The complexity of the neural control was assessed by subjects’ perceived difficulty and the resultant task performance. We developed a novel approach that used continuous estimates of ankle impedance, a quantitative description of the joint mechanics, and measures of muscle activity to determine the mechanical and neural contributions to the net ankle torque generated in each task. We found that the torque resulting from changes in neural control was reduced when ankle impedance was consistent with the task being performed. Subjects perceived this task to be easier than those that were not consistent with the impedance of the ankle and were able to perform it with the highest level of consistency across repeated trials. These results demonstrate that leveraging the mechanical properties of a joint can simplify task completion and improve performance.