Neuromuscular Dynamics Research Articles

Development of a Driver Lateral Control Model by Integrating Neuromuscular Dynamics Into the Queuing Network-Based Driver Model

This paper describes the development of a novel driver lateral control model by integrating the driver's neuromuscular dynamics into the queuing network (QN)-based driver lateral control model. Experimental results from 16 participants in a driving simulator show that, compared to the QN-based model, the proposed model performs better, and its performance is closer to that of drivers when a vehicle runs at a relatively high speed. The proposed model not only has the advantages of the models based on a cognitive architecture but also captures the dynamic interaction between the vehicular steering system and the driver's neuromuscular system. Thus, it can better represent driver lateral control and has greater value in supporting the development of driver assistance systems.

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.

The Role of a Novel Discrete-Time MRAC Based Motion Cueing on Loss of Control at a Hexapod Driving Simulator

The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict; however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.

A proprioceptive neuromechanical theory of crawling.

The locomotion of many soft-bodied animals is driven by the propagation of rhythmic waves of contraction and extension along the body. These waves are classically attributed to globally synchronized periodic patterns in the nervous system embodied in a central pattern generator (CPG). However, in many primitive organisms such as earthworms and insect larvae, the evidence for a CPG is weak, or even non-existent. We propose a neuromechanical model for rhythmically coordinated crawling that obviates the need for a CPG, by locally coupling the local neuro-muscular dynamics in the body to the mechanics of the body as it interacts frictionally with the substrate. We analyse our model using a combination of analytical and numerical methods to determine the parameter regimes where coordinated crawling is possible and compare our results with experimental data. Our theory naturally suggests mechanisms for how these movements might arise in developing organisms and how they are maintained in adults, and also suggests a robust design principle for engineered motility in soft systems.

Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics

Abstract-One of the challenging issues in computational rehabilitation is that there is a large variety of patient situations depending on the type of neurological disorder. Human characteristics are basically subject specific and time variant; for instance, neuromuscular dynamics may vary due to muscle fatigue. To tackle such patient specificity and time-varying characteristics, a robust bio-signal processing and a precise model-based control which can manage the nonlinearity and time variance of the system, would bring break-through and new modality toward computational intelligence (CI) based rehabilitation technology and personalized neuroprosthetics. Functional electrical stimulation (FES) is a useful technique to assist restoring motor capability of spinal cord injured (SCI) patients by delivering electrical pulses to paralyzed muscles. However, muscle fatigue constraints the application of FES as it results in the time-variant muscle response. To perform adaptive closedloop FES control with actual muscle response feedback taken into account, muscular torque is essential to be estimated accurately. However, inadequacy of the implantable torque sensor limits the direct measurement of the time-variant torque at the joint. This motivates the development of methods to estimate muscle torque from bio-signals that can be measured. Evoked electromyogram (eEMG) has been found to be highly correlated with FES-induced torque under various muscle conditions, indicating that it can be used for torque/force prediction. A nonlinear ARX (NARX) type model is preferred to track the relationship between eEMG and stimulated muscular torque. This paper presents a NARX recurrent neural network (NARX-RNN) model for identification/prediction of FES-induced muscular dynamics with eEMG. The NARX-RNN model may possess novelty of robust prediction performance. Due to the difficulty of choosing a proper forgetting factor of Kalman filter for predicting time-variant torque with eEMG, the presented NARX-RNN could be considered as an alternative muscular torque predictor. Data collected from five SCI patients is used to evaluate the proposed NARX-RNN model, and the results show promising estimation performances. In addition, the general importance regarding CI-based motor function modeling is introduced along with its potential impact in the rehabilitation domain. The issue toward personalized neuroprosthetics is discussed in detail with the potential role of CI-based identification and the benefit for motor-impaired patient community.

Co-Simulation of Neuromuscular Dynamics and Knee Mechanics During Human Walking

This study introduces a framework for co-simulating neuromuscular dynamics and knee joint mechanics during gait. A knee model was developed that included 17 ligament bundles and a representation of the distributed contact between a femoral component and tibial insert surface. The knee was incorporated into a forward dynamics musculoskeletal model of the lower extremity. A computed muscle control algorithm was then used to modulate the muscle excitations to drive the model to closely track measured hip, knee, and ankle angle trajectories of a subject walking overground with an instrumented knee replacement. The resulting simulations predicted the muscle forces, ligament forces, secondary knee kinematics, and tibiofemoral contact loads. Model-predicted tibiofemoral contact forces were of comparable magnitudes to experimental measurements, with peak medial (1.95 body weight (BW)) and total (2.76 BW) contact forces within 4-17% of measured values. Average root-mean-square errors over a gait cycle were 0.26, 0.42, and 0.51 BW for the medial, lateral, and total contact forces, respectively. The model was subsequently used to predict variations in joint contact pressure that could arise by altering the frontal plane joint alignment. Small variations (±2 deg) in the alignment of the femoral component and tibial insert did not substantially affect the location of contact pressure, but did alter the medio-lateral distribution of load and internal tibia rotation in swing. Thus, the computational framework can be used to virtually assess the coupled influence of both physiological and design factors on in vivo joint mechanics and performance.

Corticomuscular Transmission of Tremor Signals by Propriospinal Neurons in Parkinson's Disease

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3–C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics demonstrated a frequency dependent damping on tremor, which may prevent tremor above 8 Hz to occur.

Enhancement of neuromuscular dynamics and strength behavior using extremely low magnitude mechanical signals in mice

Exercise in general, and mechanical signals in particular, help ameliorate the neuromuscular symptoms of aging and possibly other neurodegenerative disorders by enhancing muscle function. To better understand the salutary mechanisms of such physical stimuli, we evaluated the potential for low intensity mechanical signals to promote enhanced muscle dynamics. The effects of daily brief periods of low intensity vibration (LIV) on neuromuscular functions and behavioral correlates were assessed in mice. Physiological analysis revealed that LIV increased isometric force production in semitendinosus skeletal muscle. This effect was evident in both young and old mice. Isometric force recordings also showed that LIV reduced the fatiguing effects of intensive synaptic muscle stimulation. Furthermore, LIV increased evoked neurotransmitter release at neuromuscular synapses but had no effect on spontaneous end plate potential amplitude or frequency. In behavioral studies, LIV increased mouse grip strength and potentiated initial motor activity in a novel environment. These results provide evidence for the efficacy of LIV in producing changes in the neuromuscular system that translate into performance gains at a behavioral scale.

A practical biodynamic feedthrough model for helicopters

Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the pilot’s body and cause involuntary motions of limbs, resulting in involuntary control inputs. BDFT can severely reduce ride comfort, control accuracy and, above all, safety during the operation of rotorcraft. Furthermore, BDFT can cause and sustain rotorcraft-pilot couplings. Despite many different studies conducted in past decades—both within and outside of the rotorcraft community—BDFT is still a poorly understood phenomenon. The complexities involved in BDFT have kept researchers and manufacturers in the rotorcraft domain from developing robust ways of dealing with its effects. A practical BDFT pilot model, describing the amount of involuntary control inputs as a function of accelerations, could pave the way to account for adverse BDFT effects. In the current paper, such a model is proposed. Its structure is based on the model proposed by Mayo (15th European Rotorcraft Forum, Amsterdam, pp. 81-001–81-012 1989), and its accuracy and usability are improved by incorporating insights from recently obtained experimental data. An evaluation of the model performance shows that the model describes the measured data well and that it provides a considerable improvement to the original Mayo model. Furthermore, the results indicate that the neuromuscular dynamics have an important influence on the BDFT model parameters.

Modeling the Pilot Detection of Time-Varying Aircraft Dynamics

T HE human pilot is typically required to control aircraft in which the dynamics of the vehicle vary with time. This variation may occur naturally as the aircraft changes flight condition, it can be deliberately induced by changes in the stability and command augmentation system (SCAS), or it may result from airframe damage or control system failures. The means by which the pilot is able to accommodate these changes has led to studies describing the “adaptive human pilot,” (e.g., [1–3]). The work in [4] used a simplified pursuit-control model of the human operator derived from the more complete structural pilot model of [5]. The simplification in [4] allowed implementation of an adaptive control structure in computer simulation of control tasks with time-varying controlledelement dynamics as reported in [6]. It is desirable to hypothesize the manner in which the more complete structural model might be employed to explain human pilot detection of time-varying vehicle dynamics. The structural pilot model is shown in Fig. 1 in a form suitable for a simple single-axis task, such as an aircraft/rotorcraft pitch-attitude tracking task. The pilot model includes three feedback loops defined as visual, vestibular, and proprioceptive. For the purposes of this initial discussion, the vestibular loop will be ignored (K _ m 0). A detailed description of each of the elements in the model is given in [5]. Here, with the exception of the element YPF, the elements will only be briefly described. In themajority of applications, the element Ye is a simple gain. The time delay 0 is set to 0.2 s. YNM is a simple second-order transfer function representing the neuromuscular dynamics in the particular limb that is driving the cockpit inceptor (e.g., control stick or rudder pedals). The element YFS represents the cockpit inceptor force/feel system dynamics. Yc represents the vehicle dynamics in the control axis in question (i.e., the transfer function between vehicle pitch attitude and longitudinal control inceptor input ). The proprioceptive feedback element YPF is assumed to be of the form such that

A path-following driver–vehicle model with neuromuscular dynamics, including measured and simulated responses to a step in steering angle overlay

An existing driver–vehicle model with neuromuscular dynamics is improved in the areas of cognitive delay, intrinsic muscle dynamics and alpha–gamma co-activation. The model is used to investigate the influence of steering torque feedback and neuromuscular dynamics on the vehicle response to lateral force disturbances. When steering torque feedback is present, it is found that the longitudinal position of the lateral disturbance has a significant influence on whether the driver’s reflex response reinforces or attenuates the effect of the disturbance. The response to angle and torque overlay inputs to the steering system is also investigated. The presence of the steering torque feedback reduced the disturbing effect of torque overlay and angle overlay inputs. Reflex action reduced the disturbing effect of a torque overlay input, but increased the disturbing effect of an angle overlay input. Experiments on a driving simulator showed that measured handwheel angle response to an angle overlay input was consistent with the response predicted by the model with reflex action. However, there was significant intra- and inter-subject variability. The results highlight the significance of a driver’s neuromuscular dynamics in determining the vehicle response to disturbances.

Evaluations of neuromuscular dynamics of hyperactive reflexes poststroke

Tendon reflexes are widely used in clinics to conveniently evaluate various neurological disorders. This study characterized neuromuscular dynamics of tendon reflexes at the elbow with multiple quantitative measures in both patients with stroke and nondisabled controls. We employed a handheld instrumented hammer to tap the triceps muscle tendon at various elbow flexion angles and measured the tapping force, triceps electromyography, and elbow extension torque to characterize neuromuscular dynamics of tendon reflexes quantitatively in terms of the tendon reflex gain, contraction rate, half relaxation rate, reflex loop delay, and reflex threshold in tapping force. We found that the tendon reflex gain, contraction rate, and half relaxation rate were significantly higher while the reflex threshold was significantly lower across different joint angles in patients with stroke than in nondisabled controls (p < 0.05), indicating hyperactive neurological state and muscle contraction dynamics poststroke. This study presents a convenient and quantitative method to evaluate reflex excitability and muscle contraction dynamics.

Neuromuscular dynamics in the driver–vehicle system

The work described in this article is part of a larger programme of research to understand the dynamic interaction between driver and vehicle. The driver’s neuromuscular dynamics are thought to be an important aspect of the interaction. Measurements of muscle activation voltage (electromyography, EMG) are used to identify the muscles involved in generating torque at the steering wheel. The EMG instrumentation is then developed to measure muscle co-contraction. In a lane-change manoeuvre performed on a driving simulator, muscle co-contraction is observed to occur during the manoeuvre. A mathematical model of the driver’s muscle reflex system suggests that co-contraction may be an optimal control strategy despite the extra energy required. The co-contraction increases the limb stiffness and thus the bandwidth of the control loop, which in turn allows smaller path-following errors.

The effect of visual transformation on bimanual circling movement

In order to examine the effect of visual transformation on bimanual movements of various difficulty, fourteen participants performed bimanual circling tasks in three asymmetric movement modes--90 degrees (the left hand precedes the right hand by 1/4 cycle), 180 degrees (the delay between two hands is 1/2 cycle), and 270 degrees (the left hand precedes the right hand by 3/4 cycle)--under the normal vision condition and the visual transformation condition. In the visual transformation condition, movement of the right hand was transformed so that the required bimanual movement was always presented visually as a symmetric pattern. Additionally, the participants also performed a 0 degrees mode (in-phase symmetric) movement. Results revealed that the visual transformation increased the movement accuracy, with the variability of the right-left difference unchanged. Thus, proper visual transformation can improve the accuracy of a movement task. The 0 degrees mode was performed with higher stability and accuracy than any other movement modes of the visual transformation condition and normal vision conditions. In addition, the constant error associated with the 90 degrees and 270 degrees modes indicated that, in the normal vision condition, the executed movement was shifted to the 180 degrees mode, whereas in the visual transformation condition it stayed around the required mode and was slightly shifted to the 0 degrees mode. This result suggests that visual transformation can change the relationship between the intention to realize the required mode and the intrinsic neuromuscular dynamics. The effect size of visual transformation was larger in the 90 degrees and 270 degrees modes than in the 180 degrees mode. It is thus concluded that the effect of visual transformation depends upon the difficulty of the movement task.

Model of the Neuromuscular Dynamics of the Human Pilot's Arm

This paper presents a model for the neuromuscular system of the pilot's arm. The model was developed in order to gain a better understanding of the interaction between a side stick and the human arm. In the model, the dynamics of the neuromuscular system and of the stick are considered separately, and so control situations where the stick and arm are subjected to accelerations (in a moving vehicle) or where haptic feedback is applied through the stick can be studied. Two of the experiments to provide parameter values for the model are described. One experiment is intended for determination of the parameters of the neuromuscular systems hardware, and the other for determining the control of this hardware.

System identification of tendon reflex dynamics

Patellar tendon reflexes were evaluated in 12 healthy adult subjects using several measures of the reflex responses and of the system input-output relationship. A hand-held instrumented hammer was used to tap the patellar tendon and to elicit the reflex response. Tendon reflex dynamics were estimated using the recorded tapping force (as input) and the quadriceps muscle electromyogram and knee joint extension torque signals (as output). A dome-shaped rubber pad was mounted onto the most sensitive spot on the patellar tendon, where it served as a tapping target, and helped to reduce the reflex variability significantly (p < 0.01). The input-output properties of the system relating the reflex torque to the tapping force were characterized using several measures: the tendon reflex gain (Gtr), contraction rate (Rc), and half-relaxation rate (Rhr). Reflex loop delay (t(d)) was estimated using the delay from the onset of tapping force to the onset of reflex torque. We determined that these system parameters provided significantly more repeatable and consistent characterization of tendon reflexes than did reflexive torque or EMG signals alone (p < 0.025). The input-output relationship relating the EMG signals of the stretched muscle to the tapping force was also identified to help characterize neuromuscular dynamics of tendon reflexes. The observed sensitivity and consistency of the reflex system measures suggest that with appropriate simplification of the instrumentation, these methods may prove useful in routine clinical practice, and may allow more precise quantification of the tendon jerk than is currently feasible with standard clinical tests.

Dependence of peripheral tremor on mechanical perturbations: a modeling study.

The present study scrutinizes the popular view that tremors of central origin but not those of peripheral origin are largely resistant to mechanical perturbations. We explore the effects of perturbations in a well-established model of peripheral tremor and document that (a) tremor frequency can remain unchanged when spring or weight loads are added, (b) entrainment by external drives can be limited to drives of similar frequency, and (c) resetting of tremor phase by torque pulses can remain fractional. This resistance to mechanical perturbations arises in the model because peripheral neuromuscular dynamics act as a limit-cycle oscillator which, by its very nature, will absorb moderate changes to signals and parameters. We conclude from our study that resistance to mechanical perturbations is not an exclusive property of central tremors, but rather may also be found in peripheral tremors. Other criteria are therefore needed to distinguish between different origins of tremor.

Simultaneous and nonlinear identification of mechanical and reflex properties of human elbow joint muscles.

The naturally coexisting intrinsic mechanical and reflex properties of the human elbow joint were identified simultaneously using nonlinear, time-delay, continuous-time, and dynamic models. Angular random perturbations of small amplitude and low bandwidth were applied to the joint using a computer-controlled servomotor, while the subject maintained various levels of mean background muscle torque. Joint neuromuscular dynamics were identified from the measured elbow angle and torque. Stretch reflexes were modeled nonlinearly with both dynamic and static reflex gains. A continuous-time system identification method was developed to estimate parameters of the nonlinear models directly from sampled data while retaining realistic physical or physiological interpretations. Results from six subjects showed that dynamic stretch reflex gains, joint stiffness, and viscosity generally increased with mean background muscle torque; and that dynamic stretch reflex gain was higher during muscle stretch than that during muscle shortening. More importantly, the study provided realistic simultaneous estimates of the relative contributions of intrinsic mechanical and reflex actions to net joint torque. In particular, reflexively-mediated stiffness generated a significant portion of the total joint stiffness and the percentage varied systematically with background muscle torque.

Goal-directed arm movements. II: A kinematic model and its relation to EMG records

In a previous study the EMG records of shoulder and elbow muscles during goal-directed arm movements were discussed. Here an analysis of the kinematic signals is presented and the relation between EMG and kinematic signals is assessed in a correlation analysis. The displacement as a function of time (movement trace) is analyzed. The movement trace is described by a model with five parameters that are estimated using a least squares criterion. Four parameters describe the timing of a triphasic muscular input and a fifth parameter describes neuromuscular dynamics. The parameters provide a means to compare the shape of movement traces recorded under different experimental conditions. After scaling, the shapes of movement traces with different step sizes and velocities/durations can be compared. The scaled parameters reveal a significant dependence on the movement direction of maximally fast movements. Furthermore, the scaled parameters depend significantly on the maximum velocity obtained by the subjects. This dependence can be interpreted in terms of neural inputs and muscular dynamics, as the parameters have been defined in these terms. The parameters estimated from the kinematics have a high correlation with EMG timing. A further analysis of these correlations indicates that the movement obtained is closely coupled to activity of the four prime movers of the upper arm and of three out of four scapular muscles.

筋運動制御機構を持つロボット

筋運動制御機構を持つロボット