Human Control Model Research Articles

Feedback Control of Functional Electrical Stimulation for 2-D Arm Reaching Movements.

Functional electrical stimulation (FES) can be used as a neuroprosthesis in which muscles are stimulated by electrical pulses to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. This paper introduces a feedback controller for a multi-muscle FES system to control hand movements in a 2-D (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify its calculations and improve the performance. This synergy-based controller employs direct relations between the muscle synergies and the produced hand force, therefore allowing for the real-time calculation of six muscle stimulation levels required to reach an arbitrary target. The experimental results show that this control scheme can perform arbitrary point-to-point reaching tasks in the 2-D task space in real time, with an average of ~2 cm final hand position error from the specified targets. The success of this prototype demonstrates the potential of the proposed method for the feedback control of functional tasks with FES.

Human-centric predictive model of task difficulty for human-in-the-loop control tasks.

Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control.

Development of a human head and neck muscle activation control model based on BPNN

With the development of vehicle active safety technology and its combination with passive safety technologies, there is a need for an updated tool to assess the performance of vehicle safety systems in both the in-crash and pre-crash phases. Consequently, developing human body models with active muscles is critical to achieve improved bio-fidelity simulation results to evaluate the effects of safety systems. In the present study, an innovative human active muscle control model based on a back-propagation neural network (BPNN) controller was developed, and the active muscle response was modelled using a feedback control strategy with the BPNN algorithm implemented in a human head and neck Sim Mechanics model. The BPNN controller was used to dynamically control the muscle activation level, the most complicated and essential factor in active muscle force calculation based on the Hill equation. Then, the active muscle control model was evaluated by simulating the human response in volunteer sled tests. The results validated the proposed active muscle control method as successful and easily realized. Achieving muscle active control would further improve the bio-fidelity of the human body model.

Moment stability analysis of linear stochastic human controller model in visuomotor tracking task

We investigate the stability limits of linear stochastic human controller model. In our previous study, we had proposed a model by which the probability distributions of human visuomotor tracking data can be accurately reproduced. In this study, we conduct a stochastic analysis on our model by deriving a system of moment differential equations with respect to random state variables of this model. We evaluate the stability limits of our model by detecting zero-eigenvalue conditions of the Jacobian matrix of the moment differential equations. The resulting stability limits make it possible to characterize the human parameter values that were experimentally identified from the human participants in our previous study; this shows that during the visuomotor tracking experiments, the human participants generated huge fluctuations far from the stability limits while applying almost neutrally stable proportional gain.

An Engineering Model to Test for Sensory Reweighting: Nonhuman Primates Serve as a Model for Human Postural Control and Vestibular Dysfunction.

Quantitative animal models are critically needed to provide proof of concept for the investigation of rehabilitative balance therapies (e.g., invasive vestibular prostheses) and treatment response prior to, or in conjunction with, human clinical trials. This paper describes a novel approach to modeling the nonhuman primate postural control system. Our observation that rhesus macaques and humans have even remotely similar postural control motivates the further application of the rhesus macaque as a model for studying the effects of vestibular dysfunction, as well as vestibular prosthesis-assisted states, on human postural control. Previously, system identification methodologies and models were only used to describe human posture. However, here we utilized pseudorandom, roll-tilt balance platform stimuli to perturb the posture of a rhesus monkey in normal and mild vestibular (equilibrium) loss states. The relationship between rhesus monkey trunk sway and platform roll-tilt was determined via stimulus-response curves and transfer function results. A feedback controller model was then used to explore sensory reweighting (i.e., changes in sensory reliance), which prevented the animal from falling off of the tilting platform. Conclusions involving sensory reweighting in the nonhuman primate for a normal sensory state and a state of mild vestibular loss led to meaningful insights. This first-phase effort to model the balance control system in nonhuman primates is essential for future investigations toward the effects of invasive rehabilitative (balance) technologies on postural control in primates, and ultimately, humans.

Feedback control of functional electrical stimulation for arbitrary upper extremity movements.

Functional electrical stimulation (FES) is a type of neuroprosthesis in which muscles are stimulated by electrical pulses in order to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. For the first time, this paper presents a feedback controller for FES to control arm movements in a 2D (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify the calculations and improve control performance. The experimental results show that this control scheme can produce arbitrary movements in the 2D task space, with less than 2 cm hand position error from the specified targets.

Extended Crossover Model for Human-Control of Fractional Order Plants

A data-driven generalization of the crossover model is proposed, characterizing the human control of systems with both integer and fractional-order plant dynamics. The model is developed and validated using data obtained from human subjects operating in compensatory and pursuit tracking tasks. From the model, it is inferred that humans possess a limited but consistent capability to compensate for fractional-order plant dynamics. Further, a review of potential sources of fractionality within such man–machine systems suggests that visual perception, based on visual cues that contain memory, and muscular dynamics are likely sources of fractional-order dynamics within humans themselves. Accordingly, a possible mechanism for fractional-order compensation, operating between visual and muscular sub-systems, is proposed. Deeper analysis of the data shows that human response is more highly correlated to fractional-order representations of visual cues, rather than directly to objective engineering variables, as is commonly proposed in human control models in the literature. These results are expected to underpin future design developments in human-in-the-loop cyber-physical systems, for example, in semi-autonomous highway driving.

Constraints in Identification of Multi-Loop Feedforward Human Control Models

Abstract: The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three central issues that complicate this objective. First, the identification method should not require prior assumptions regarding the dynamics of the feedforward and feedback components. Second, severe biases might be introduced by high levels of noise in the data measured under closed-loop conditions. To address the first two issues, we will consider two identification methods that make use of linear ARX models: the classic direct method and the two-stage indirect method of van den Hof and Schrama (1993). Third, model complexity should be considered in the selection of the ‘best’ ARX model to prevent ‘false-positive’ feedforward identification. Various model selection criteria, that make an explicit trade-off between model quality and model complexity, are considered. Based on computer simulations with a HC model, we conclude that 1) the direct method provides more accurate estimates in the frequency range of interest, and 2) existing model selection criteria do not prevent false-positive feedforward identification. Copyright ©2016 IFAC

An Empirical Human Controller Model for Preview Tracking Tasks.

Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.

Human control model in teleoperation rendezvous

This paper proposes a human control model in teleoperation rendezvous on the basis of human information processing (perception, judgment, inference, decision and response). A predictive display model is introduced to provide the human operator with predictive information of relative motion. By use of this information, the longitudinal and lateral control models for the operator are presented based on phase plane control method and fuzzy control method, and human handling qualities are analyzed. The integration of these two models represents the human control model. Such a model can be used to simulate the control process of the human operator, which teleoperates the rendezvous with the aid of predictive display. Experiments with human in the loop are carried out based on the semi-Physical simulation system to verify this human control model. The results show that this human control model can emulate human operators’ performance effectively, and provides an excellent way for the analysis, evaluation and design of the teleoperation rendezvous system.

OPTIMAL RESPIRATORY CONTROL SIMULATION AND COMPARATIVE STUDY OF HYPERCAPNIC VENTILATORY RESPONSES TO EXTERNAL DEAD SPACE LOADING

There has been considerable research effort regarding ventilatory responses to breathing with an imposed external dead space, and inhalation of fixed levels of CO 2 by human subjects. A human respiratory control model incorporating the optimality hypothesis can successfully demonstrate ventilatory responses to both chemical stimuli and muscular exercise. In this study, to verify the model behavior of the optimal chemical–mechanical respiratory control model, we simulated the ventilatory control under dead space loading and CO 2 inhalation. The simulation was provided by a LabVIEW® based human respiratory control simulator and signal monitoring system. The dead space measurement was described with two distinct models, derived from Gray and Coon, and predicted behaviors with corresponding ventilatory responses were investigated and compared with experimental findings. While both dead space models produced satisfactory predictions on simulated optimal [Formula: see text] versus Pa CO 2, [Formula: see text] versus Pa CO 2, F versus PI CO 2, VT versus PI CO 2, VD-total versus VT, VD- total /VT versus VT, [Formula: see text] versus VT and [Formula: see text] versus VT relationships, Gray's model provided better correlation and more consistent results throughout most of the ventilatory responses. The study of relative behavior of respiratory signals and comparative relationship of the ventilator responses between dead space loading during rest and CO 2 inhalation will certainly provide valuable understanding of increases in central respiratory motor command output of human respiratory control, which is also associated with Dyspnea on exertion, and give potential clinical perspective to realize the impaired ability to excrete CO 2 in patients diagnosed with acute respiratory distress syndrome.

Balance and Control of a Rear-wheel Steered Speed-record Recumbent Bicycle

The goal of the Human Power Team from the TU Delft and the Free University of Amsterdam is to break the world speed record in unpaced cycling (Sam Whittingham, 133.28km/h). The design of such a faired recumbent bicycle is a challenge. The Delft design, called VeloX (Human Power Team (2013)), is a fully-faired monocoque front-driven recumbent bicycle, with minimized air drag and maximized space for a big and strong athlete. However, front driven bicycles have the disadvantage that the front driving induces unwanted steering and that the frontal area of the bicycle cannot be reduced any further. A solution would be rear-wheel steering. A common thought is that a rear-wheel steered bicycle cannot be laterally self-stable, and therefore hard to control. However, recent research (Knoll et al. (2012)) has shown that one can design a rear-wheel steered bicycle which shows a stable forward speed range.Based on these results a rear-wheel steered recumbent bicycle has been designed, within the existing design constraints. Although not self-stable, this design shows a mildly lateral unstable behavior in the desired forward speed range of 0 to 40 m/s (0 to 144km/h). Computer simulations demonstrate that the bicycle can be stabilized by adding a human controller model (Schwab et al. (2013)) to the bicycle model. For a set of expected lateral perturbations (side wind perturbations) it is shown that rider steer torque stays within human bounds, both in magnitude and in frequency. Future work is dedicated to building and testing a prototype of the design.

A survey of 3D object selection techniques for virtual environments

Computer graphics applications controlled through natural gestures are gaining increasing popularity these days due to recent developments in low-cost tracking systems and gesture recognition technologies. Although interaction techniques through natural gestures have already demonstrated their benefits in manipulation, navigation and avatar-control tasks, effective selection with pointing gestures remains an open problem. In this paper we survey the state-of-the-art in 3D object selection techniques. We review important findings in human control models, analyze major factors influencing selection performance, and classify existing techniques according to a number of criteria. Unlike other components of the application's user interface, pointing techniques need a close coupling with the rendering pipeline, introducing new elements to be drawn, and potentially modifying the object layout and the way the scene is rendered. Conversely, selection performance is affected by rendering issues such as visual feedback, depth perception, and occlusion management. We thus review existing literature paying special attention to those aspects in the boundary between computer graphics and human–computer interaction.

Needle visibility in different tissue models for ultrasound‐guided regional anaesthesia

Models for ultrasound-guided regional anaesthesia (USGRA) are important for research and training. However, the limited data available show great differences in quality of needle and tissue visualisation with regard to the applied model. This study aims to compare common USGRA models and human tissue with regard to their influence on needle visibility. We conducted this study using four models (embalmed human cadaver, turkey breast, pork, and synthetic gel models) and a volunteer (human control) as well as two different needles [Stimuplex A (StA), conventional needle; Stimuplex D Plus (StD+), needle with improved echogenicity]. We obtained ultrasound videos of needle advancement and withdrawal using the in-plane approach at a 45° angle in all four models and the volunteer. Fifteen test persons were prospectively enrolled. Ultrasound videos were presented in a randomised, blinded manner. The test persons were asked to rate the visibility of the needle shaft (VS) and tip (VT) on a four-point scale (0-3). VS and VT were comparable between the human control and cadaver model for both needle types. The pork, turkey, and synthetic gel models had significantly higher visibility scores than the human control for both needle types. VS of StD+ was significantly higher than that of StA in the pork and turkey models, but not in the synthetic model, cadaver model, or human control. In this pilot study, needle visibility in embalmed cadaver is comparable with that in human control. Needle visibility was significantly higher in other tissue models (turkey breast, pork, synthetic gel models) than in the human control, which may limit their value in training environments.

A Two-Joint Human Posture Control Model With Realistic Neural Delays

During quiet standing, humans tend to sway with a distinctive pattern that has been difficult to capture with simple engineering models. We have developed a nonlinear optimal control model for posture regulation. The proposed model consists of two main components: body dynamics and performance measure. The body dynamics are those of a double inverted pendulum in the sagittal plane controlled by ankle and hip torques. The performance measure is nonlinear quartic in the center of pressure and quadratic in the controls. Realistic values for both sensory and motor delays are included in the dynamic model. This nonlinear quartic regulator problem is solved approximately by the model predictive control technique. The resulting feedback control replicates both the experimentally observed sway and the coordinated nonlinear response. It should also use less muscular energy than other comparable controls. The method can easily be extended to more complex models of posture regulation.

8A14 二重振子の間欠制御を用いたヒト静止立位姿勢制御のモデル化(OS1 筋骨格系のバイオメカニクス3)

8A14 二重振子の間欠制御を用いたヒト静止立位姿勢制御のモデル化(OS1 筋骨格系のバイオメカニクス3)

Special issue on human–automation coagency

In human supervisory control, machines control the process according to directives given by human operators, where the humans have the final authority over the machines or automation. There are many human–machine systems in our society that are modeled nicely by the human supervisory control model. A glass-cockpit passenger aircraft is a typical example of such a system. However, even in transportation, human–automation relation for technologically advanced automobile may be quite different from that for a glass-cockpit aircraft. In automobile, it is only the automation that may be able to help the driver by backing up or replacing him/her to assure safety: Neither air traffic controller-like function nor copilot-like colleague is available there (Inagaki 2010). An advanced driver assistance system (ADAS) is a machine to assist a human to drive a car in a dynamic environment. Some functions of ADAS may include: (a) perception enhancement that helps the driver to perceive the traffic environment around his/her vehicle, (b) arousing attention of the driver to encourage paying attention to potential risks around his/her vehicle, (c) setting off a warning to urge the driver to take a specific action, and (d) automatic safety control that is activated when the driver takes no action even after being warned or when the driver’s control action seems to be insufficient. The first two functions, (a) and (b), are to help the driver to recognize or understand the situation. Understanding of the current situation determines what action needs to be done (Hollnagel and Bye 2000). Once situation diagnostic decision is made, action selection is usually straightforward (Klein 1993). However, the driver may not be successful in action selection decision. Function (c) is to help the driver in such a circumstance. Any ADAS that uses only the three functions, (a)–(c), is completely compatible with the human-centered automation principle (Billings 1997) in which the human is assumed to have the final authority over the automation. Suppose an ADAS contains the forth function, (d). Then, the ADAS may not be fully compatible with the human-centered automation, because the automation can implement a safety control action without any human intervention. Should we ban such an ADAS just because it can implement an action that is not ordered by the driver? It is well known that highly automated machines sometimes bring negative effects, such as the out-of-the-loop performance problem, loss of situation awareness, complacency or overtrust, automation surprises. However, humans have limited capabilities, and they might fail to understand the situation, select a right action and implement it appropriately, especially when available time and information are quite limited. Today’s machine can sense and analyze a situation, decide what must be done, and implement control actions. Cannot such a smart machine help the humans in more a positive manner as a teammate to the humans? The systems in which humans and technology collaborate together to achieve a common goal are called joint cognitive systems (Hollnagel and Woods 2005), in which human–automation coagency is central for realizing sensible human–automation partnership. This special issue of Cognition Technology and Work intends to discuss human–automation coagency for collaborative control of automobile. The first topic is authority and responsibility. The first three papers argue the need of situationand context-dependent sharing of authority between the human and the automation, without assuming T. Inagaki (&) Department of Risk Engineering, University of Tsukuba, Tsukuba 305-8573, Japan e-mail: inagaki@risk.tsukuba.ac.jp

Analysis and Design of a Wearable Robot for Lower-Limb Rehabilitation Training

After studying the advantages and disadvantages of existing wearable lower limb rehabilitation training robot product performance, by establishing human movement control model and the quadratic approximation formula, we designed a kind of control high-precision of lower limb rehabilitation training robots. The robot can simulate the normal actions as sitting, standing and walking for patients to take rehabilitation training. The structure of it is simple and reliable. And it is easily to be manufactured. The robot provides an ideal device for lower limb rehabilitation training

Human-inspired robot as a platform for comparing between human stance control models

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613 人の動的バランス制御能力の評価

This paper investigates the relation between the dynamic balance ability and the age of human. The purpose of this research is quantification of dynamic balance ability of human by identification of the model parameters of the human balance control model. First, subjects of various age were measured to examine the relation between the dynamic balance ability and the age. Subjects controlled balance on a seesaw. Second, the dynamic balance ability was quantified by identification of the model parameters. Third, experimental results showed that the relations between model parameters and the age were not significant. Finally, the relation between the fitting error and the age was examined, and the fitting error increases with age.