Search

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]

This paper describes the implementation of fast state-dependent Riccati equation (SDRE) control algorithms through the use of shallow and deep artificial neural networks (ANN). Several ANNs are trained to replicate an SDRE controller developed for a satellite attitude dynamics simulator (SADS) to display the technique's efficacy. The neural controllers have reduced computational complexity compared with the original SDRE controller, allowing its execution at a significantly higher rate. One of the neural controllers was validated using the SADS in a practical experiment. The experimental results indicate that the training error is sufficiently small for the neural controller to perform equivalently to the original SDRE controller. • A real-time SDRE controller is designed using shallow and deep neural networks. • A significant reduction in computation is achieved using the neural controllers. • Deep denoising autoencoders were trained as deep neural controllers. • The neural controller is validated through simulations and a practical experiment. • Results show that the neural controller retains its high performance. [ABSTRACT FROM AUTHOR]

Highlights • Maximum voluntary eccentric forces can exceed maximum isometric forces at the same muscle length by a factor of 1.2–1.4, provided that the experimental conditions result in active fascicle stretch during the eccentric contractions. • Muscle fascicle length, velocity, and stretch amplitude all interact to determine voluntary eccentric force capacity. • Apparent neural inhibition during maximal voluntary eccentric contractions has not been confirmed under conditions where eccentric forces exceed isometric forces at identical muscle length. • The reduction in voluntary eccentric force capacity relative to the eccentric forces obtained from electrically stimulated contractions and from isolated muscle preparations remains unclear. Abstract During eccentric contractions, muscular force production capacity is enhanced compared to isometric contractions. Although this is well accepted in terms of muscle mechanics, maximal voluntary eccentric contractions are associated with neural inhibition that prevents increased force production of in vivo human muscles. However, because it was shown that maximal voluntary eccentric forces can exceed maximum isometric forces by a factor of 1.2–1.4, this review focuses on the question of whether the absent eccentric force enhancement, as observed in many studies, can unambiguously be attributed to an inherent neural inhibition. First, we demonstrate that participant familiarization, preload, and fascicle behavior are crucial factors influencing maximal voluntary eccentric force production. Second, we show that muscle mechanics such as muscle length, lengthening velocity, and stretch amplitude interact when it comes to maximal voluntary eccentric force production. Finally, we discuss the diverging findings on neural inhibition during maximal voluntary eccentric contractions. Because there was no inhibition of the major motor pathways in the presence of enhanced maximal voluntary eccentric forces, further research is needed to test the concept of neural inhibition and to understand why maximal voluntary force production is reduced compared to the force capacity of isolated muscle preparations. [ABSTRACT FROM AUTHOR]

* STUDY DESIGN: Descriptive, prospective single-cohort longitudinal study. * BACKGROUND: Though rapid torque development is essential in activities of daily living and sports, it hasn't been specifically tested by most physical therapists or incorporated into rehabilitation programs until late in the treatment process. Little evidence is available on quadriceps torque development capacity before and after arthroscopic knee surgery. * OBJECTIVES: To study knee extensor rate of torque development, contributing mechanisms, and associations with strength and patient-reported outcomes before and during the first 6 weeks after arthroscopic partial meniscectomy. * METHODS: Twenty subjects (mean ± SD age, 42.3 ± 13.7 years; body mass index, 26.6 ± 3.1 kg/ m²) were tested before surgery, and at 2 and 5 weeks after surgery. Quadriceps muscle volume, strength, activation, rate of torque development, and patient-reported outcomes were evaluated across the study period. * RESULTS: Significant side-to-side differences in quadriceps strength and voluntary rate of torque development were observed at each time point (P<.05). Changes in muscle activity were associated with changes in rapid torque development capacity. Side-to-side rate of torque development deficits after surgery were associated with lower patient-reported outcomes scores. * CONCLUSION: Diminished rapid torque development capacity is common in arthroscopic meniscal debridement patients. This reduced capacity is associated with an inability to quickly recruit and drive the quadriceps muscles (neural mechanisms) and not muscle atrophy or other peripheral factors tested. Patient-reported outcomes are associated with quadriceps rate of torque development, but not strength or muscle size. Rapid torque development warrants greater attention in rehabilitation. [ABSTRACT FROM AUTHOR]

Optimization of gait rehabilitation using split-belt treadmills critically depends on our understanding of the roles of somatosensory perception and sensorimotor recalibration in perceiving gait asymmetry and adapting to split-belt walking. Recent evidence justifies the hypothesis that perception of gait asymmetry is based mainly on detection of temporal mismatches between afferent inputs at the spinal level. [ABSTRACT FROM AUTHOR]

Urinary incontinence is a condition with multifaceted etiology and a significant health issue especially for aging men and women. The purpose of this report is to review the anatomy of the lower urinary tract and pelvic floor, along with the current understanding of the neural control pathways, which act reciprocally at all levels of the nervous system to control the pelvic floor and urinary bladder to maintain urinary continence. Understanding all of these elements and how they affect pelvic floor function, urethral function, or urinary bladder function will assist rehabilitation professionals in addressing this potentially devastating health problem. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for the synthesis of neural networks to control gyrostabilizer. The neural network performs the role of a state vector observer. The role of such an observer is to provide feedback on gyrostabilizer, which is illustrated in the article. The paper details the issue of specific stage-related peculiarities of classic algorithms: choosing the network architecture, learning the neural network and verifying the results of feedback control. The article presents an optimal configuration of the neural network like memory depth, number of layers and neurons in these layers, and activation layer functions. It also provides data on dynamic systems to improve learning neural network learning. There is also provided an optimal training pattern. [ABSTRACT FROM AUTHOR]

Background/purpose: To compare the neuromechanical characteristics and subjective outcomes for knees of patients with a cruciate ligament tear and reconstruction with those for knees of controls at three time intervals, and to determine correlations between the characteristics and subjective outcomes.Methods: Ten participants with a cruciate ligament tear and at least a 12-week conservative treatment prior to ligament reconstruction were prospectively measured prior to and 3 months and 6 months after surgery. Ten healthy individuals were recruited as controls. Questionnaire surveys regarding the injured knee were conducted, as were bilateral measurements of root mean square electromyography (EMG), the rate of EMG rise, the median frequency in the vastus medialis of the quadriceps muscles and antagonist coactivation from the semitendinosus muscle, and force capacities, including peak torque, rate of force development, and total works of the knee extension. Correlations between the EMG variables (of the vastus medialis and semitendinosus) and the force capacities, and between the EMG variables and the knee injury and osteoarthritis outcome scores (KOOS), and between force capacities and the KOOS were assessed in the participants with a ligament reconstruction.Results: Pre- and postoperative results of EMG variable and force capacities were lower in both knees of the experimental group participants than in the control group participants (all p < 0.05). Correlations between EMG and force capacities, and between these parameters and the KOOS were found.Conclusion: There were bilateral neuromechanical defects in the knees of the participants who had undergone conservative treatment as well as reconstruction after a cruciate ligament tear. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L2-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L2-gain robust control approach by the proposed ICSMC scheme. [ABSTRACT FROM AUTHOR]

Highlights: [•] The proposed self-evolving functional-linked wavelet neural network (SFWNN) can vary its structure dynamically to keep the prescribed approximation accuracy with a simple computation. [•] This paper presents an adaptive self-evolving functional-linked wavelet neural control (ASFWNC) for a class of uncertain nonlinear systems. [•] The proposed ASFWNC system is applied to a chaotic dynamic and a DC motor. [•] Simulation and experimental results verify that the proposed ASFWNC system can achieve high-precision tracking performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications. [Copyright &y& Elsevier]

Abstract: Based on the principle of sliding mode control and the property of Nussbaum-type functions, an adaptive neural control scheme is proposed for a class of MIMO nonlinear time delay systems with unknown function control gains. By utilizing the integral-type Lyapunov function and Young''s inequality, the restriction of the control gains and the assumption of time-varying delay uncertainties are relaxed. By choosing appropriate Lyapunov–Krasovskii functionals, unknown time-varying delay uncertainties are compensated for. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. [Copyright &y& Elsevier]

Abstract: In this paper, an adaptive DRBF neural control (ADNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller utilizes a dynamic radial basis function (DRBF) network to online mimic an ideal controller and the smooth compensator is designed to eliminate the effect of the approximation error between the ideal controller and neural controller. The DRBF network can self-organizing its network structure. All the controller parameters of the proposed ADNC system are online tuned in the Lyapunov sense, thus the stability analytic shows the system output can exponentially converge to a small neighborhood of the trajectory command. Finally, the proposed ADNC system is applied to a chaotic system and a DC motor. Simulation and experimental results verify that a favorable tracking performance and no chattering phenomena can be achieved by the proposed ADNC system. [Copyright &y& Elsevier]

There is a great diversity of ways to use fuzzy inference in robot control systems, either in the place where it is applied in the control scheme or in the form or type of inference algorithms used. On the other hand, artificial neural networks ability to simulate nonlinear systems is used in different researches in order to develop automated control systems of industrial processes. In these applications of neural networks, there are two important steps: system identification (development of neural process model) and development of control (definition of neural control structure). In this paper we present some modelling applications, which uses fuzzy and neural controllers, developed on a pneumatic actuator containing a force and a position sensor, which can be used for robotic grinding operations. Following the simulation one of the algorithms was tested on an experimental setup. The paper also presents the development of a NARMA-L2 neural controller for a pneumatic actuator using position feedback. The structure had been trained and validated, obtaining good results. [ABSTRACT FROM AUTHOR]

Abstract: The bladder has two functions: to store urine and to periodically void the contents. The lower urinary tract has two components that must act in a coordinated way to achieve these functions: the urinary bladder and a competent sphincter mechanism. Urine storage is possible when the bladder is relaxed and compliant, and the sphincter offers a high resistance. Voiding occurs when the sphincter relaxes, and the bladder contracts to raise intravesical pressure. Urodynamics can illustrate these changes of function. Control of this coordinated system is mediated by a nervous pathway involving pontine centres that in turn exert control over a sacral reflex. The periaqueductal grey and pontine micturition centre play key roles in this central nervous control. Efferent control is achieved via autonomic and somatic pathways from the sacral spinal cord to the bladder and sphincter. Incontinence occurs when the system is deranged and may be due to bladder overactivity, sphincter dysfunction or bladder underactivity. There are many causes of these dysfunctions and drug management offers solutions in some cases. [Copyright &y& Elsevier]

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control. [Copyright &y& Elsevier]

In this paper, the stability of a class of systems arising from neural control and fuzzy systems is studied. A new unifying stability criterion is presented using a very simple derivation. This result generalizes some previous results; some easily testable conditions are obtained. [ABSTRACT FROM AUTHOR]