Motion Fidelity Research Articles

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy.

The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model's desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

An Optimal Nonlinear Model Predictive Control- Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

Is High-Fidelity Important for Human-like Virtual Avatars in Human Computer Interactions?

Survey/review study Is High-Fidelity Important for Human-like Virtual Avatars in Human Computer Interactions? Qiongdan Cao , Hui Yu ,*, Paul Charisse , Si Qiao , and Brett Stevens 1 University of Portsmouth, Portsmouth, PO1 2UP, UK * Correspondence: hui.yu@port.ac.uk Received: 2 March 2023 Accepted: 6 March 2023 Published: 27 March 2023 Abstract: As virtual avatars have become increasingly popular in recent years, current needs indicate that “interactivity” is crucial for inducing a positive response from users towards these avatars, especially in human computer interactions (HCI). This paper reviews recent works on high-fidelity human-like virtual avatars (e.g. high visual and motion fidelity) and discusses the critical question——“Is high-fidelity a positive choice for virtual avatars to achieve better interactions in HCI”. Furthermore, we summarise current technical approaches to developing those virtual avatars. We investigate the advantages and disadvantages of high-fidelity virtual avatars in different areas focusing on addressing the effect of motion, especially the upper body. Research shows that high-fidelity is a positive choice for virtual avatars, although it may depend on the application.

Effect of Motion Cues on Highway Driving Performance in Simulated Driving

The motion system is a major component of a driving simulator. It provides motion cues in various directions with different degrees of freedom (DOF). Different DOF could influence response variables widely used in highway design safety evaluation conducted in simulators. Few studies, though, have comprehensively explored the effect of driving simulator motion cues on driving performance. The high-fidelity Tongji University Driving Simulator (TUDS) was used to separately compare five dependent variables: speed, speed change, lane deviation, longitudinal acceleration, and lateral acceleration. The Wilcoxon signed-rank test was used to examine the difference for each variable using 8-DOF and fixed-base motion system configurations for nine types of combined highway alignments. A subjective assessment of participants’ driving experience was conducted, and the chi-square test for each term was used to compare the difference in motion system fidelity and adverse reactions between the two configurations. Results show that: (1) speed and lateral acceleration are more likely to be significantly affected by motion cues than speed change, lane deviation, and longitudinal acceleration; (2) the horizontal curve with up-grade is the type of combined alignment where speed and lateral acceleration are mostly affected; (3) characteristics of the five variables in different combined alignments vary between the two simulators; (4) the 8-DOF motion system provides better perceived motion fidelity than the fixed-base system does, and the 8-DOF simulator causes less discomfort and tiredness in simulator sickness; and (5) the presence of motion cues is suggested for highways with complex combined curves when focusing on speed and lateral acceleration.

Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.

A Fusion Crowd Simulation Method: Integrating Data with Dynamics, Personality with Common

AbstractThis paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data‐driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re‐described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.

DeepORCA: Realistic crowd simulation for varying scenes

AbstractCrowd simulation is a challenging problem, aiming to generate realistic pedestrians motions in virtual environment. Nowadays, ORCA is a widely used simulation algorithm in practice because of its stable and efficient performance. However, this algorithm cannot regenerate continuity and diversity of pedestrian motions in real data, leading to defects in motion fidelity. Otherwise, trajectory prediction methods based on deep learning have progressed in real pedestrians movement patterns mining. However, they are rarely applied in simulation due to the lack of ability to avoid collision and adapt to manufactured scenarios. Our work proposes a simulation method DeepORCA that integrates ORCA with a CVAE‐based velocity probability generator, which can model motion continuity, variable intentions, and scene semantics. Moreover, DeepORCA converts the velocity optimization into quadratic programming, which accelerates the calculation while maintaining the collision‐avoidance ability of ORCA. In the experiments of real and artificial scenes, our method produces more realistic crowd simulation results than ORCA quantitatively and qualitatively, while keeps the computational efficiency at the same order of magnitude.

Auto-Tuning Parameters of the Offline Optimal Motion Cueing Algorithm with Mean-Variance Mapping Optimization

A motion cueing algorithm (MCA) not only maintains the simulator within its physical limits but also generates such movements of the driving simulator that the necessary motion cues of drivers on the realistic vehicle are equivalently reproduced. The offline optimal MCA focuses on finding the best combination of the translational acceleration and tilt angles of the motion platform to maintain drivers’ motion perception. However, the best combination depends on the MCA’s parameters, tuned mainly by trial and error with experts in the loop. Moreover, for different amplitude input signals, the parameters are accordingly modified. This manual tuning procedure is so time-consuming that the generic optimization, named Mean-Variance mapping optimization, was proposed to search the suitable parameters for the optimal algorithm. This tuning method uses the specific cost function of constraint conditions such as workspace limits, avoiding false cues, and improving motion fidelity to achieve the best parameters for optimal MCA with the particular input signals.

Design and Kinematics Analysis of Disc Cam with Translation Flat-face Follower

The profile equation of disc cam with translation flat-face follower is derived, the motion fidelity analysis is carried out, and the minimum size range of flat-face is deduced based on the rotation transformation tensor and meshing principle. The coordinate data of cam profile point is calculated by VC++, and the cam modeling and mechanism assembly are carried out with Creo software. The kinematics simulation analysis is completed, and the displacement, velocity and displacement of follower are obtained. The simulation analysis verifies the correctness of the design method, and the research has practical significance for the design of the disc cam with translation flat-face follower.

Between-Simulator Comparison of Motion-Filter Order and Break Frequency Effects on Manual Control

This paper investigates the interaction effects of motion filter order and break frequency on pilots’ manual control behavior and control performance using two simulators. Eighteen pilots performed the experiment in the Vertical Motion Simulator (VMS) at NASA Ames Research Center and 20 pilots in the SIMONA Research Simulator at Delft University of Technology. The experiment used a full-factorial design with three motion filter orders (first-, second-, and third-order) and two filter break frequencies (0.5 and ), in addition to reference no-motion and full-motion conditions. Key task variables, such as the quality of the motion and visual cues and the characteristics of the sidestick, were matched across both simulators. Overall, the expected effects of filter order and break frequency variations were found, with both increasing order and increasing break frequency causing pilots to use less motion feedback in their control strategy, resulting in lower tracking performance. Furthermore, across the wide range of filter orders tested in the experiment, the existing Sinacori–Schroeder motion fidelity criterion was found to be a good predictor of the interaction effects of both filter settings on pilot control behavior. For the same motion condition, there was a consistent offset in the results between simulators, due to the more high-gain control strategy adopted by a number of the VMS pilots. Still, the observed relative trends in pilot control behavior and performance between motion conditions were equivalent in both simulators and thus accurately replicated.

On the Objective Evaluation of Motion Cueing in Vehicle Simulations

Motion-based simulators are used for a variety of applications, such as research, education, entertainment and training. In fact, motion cues are required to achieve the highest regulatory certifications in training vehicle simulators. Nonetheless, the reproduction of self-motion cues presents technological and economic limitations that are not present in the generation of audiovisual cues. For this reason, the generated motion does not generally match the expected one. Therefore, it is necessary to define means to assess the suitability/fidelity of the generated motion cues. After more than 50 years of motion-based vehicle simulation, no mechanism has been universally accepted as the standard solution for the evaluation of motion cues. This paper reviews the mechanisms for obtaining measures of motion fidelity, focusing on those based on objective methods. Since the design of the Objective Motion Cueing Test in 2006, researchers have shown a renewed interest in identifying objective methods to evaluate motion cueing, as the number of works following this approach in recent years reveals. Objective motion fidelity systems allow also performing automatic tuning of MCA by means of optimization techniques, which addresses one of the other main problems of these algorithms. Nevertheless, a universally accepted objective method to assess perceptual motion fidelity in vehicle simulators has not been proposed yet. For this reason, this review work frames and classifies the existing methods. In addition, the authors propose a series of features that an ideal evaluation method for assessing perceptual motion fidelity should include and provide future research guidelines for this complex topic.

A New Gantry-Tau-Based Mechanism Using Spherical Wrist and Model Predictive Control-Based Motion Cueing Algorithm

SUMMARYThe 3 degree-of-freedom Gantry-Tau manipulator with the addition of the spherical wrist mechanism which is called Gantry-Tau-3R is designed as a high-G simulation-based motion platform (SBMP) with the capability of generating the large linear and angular displacement. The combination of both parallel and serial manipulator in newly designed Gantry-Tau-3R mechanism improves the ability of the mechanism to regenerate larger motion signals with higher linear acceleration and angular velocity. The high-frequency signals are reproduced using the parallel part of the mechanism, and sustainable low-frequency accelerations are regenerated via the serial part due to the larger rotational motion capability, which will be used through motion cueing algorithm tilt coordination channel. The proportional integral derivative (PID) and fuzzy incremental controller (FIC) are developed for the proposed mechanism to show the high path tracking performance as a motion platform. FIC reduces the motion tracking error of the newly designed Gantry-Tau-3R and increases the motion fidelity for the users of the proposed SBMP. The proposed method is implemented using Matlab/Simulink software. Finally, the results demonstrate the accurate motion signal generation using linear model predictive motion cues with a fuzzy controller, which is not possible using the common parallel and serial manipulators.

Offline motion simulation framework: Optimizing motion simulator trajectories and parameters

This paper presents a method to simultaneously compute optimal simulator motions and simulator parameters for a predefined set of vehicle motions. The optimization can be performed with a model of human motion perception or sensory dynamics taken into account. The simulator dynamics, sensory dynamics, and optimality criterion are provided by the user. The dynamical models are defined by implicit index-1 differential-algebraic equations (DAE). The direct collocation method is used to find the numerical solution of the optimization problem. The possible applications of the method include calculating optimal simulator motion for scenarios when the future motion is perfectly known (e.g., comfort studies with autonomous vehicles), optimizing simulator design, and evaluating the maximum possible cueing fidelity for a given simulator. To demonstrate the method, we calculated optimal trajectories for a set of typical car maneuvers for the CyberMotion Simulator at the Max-Planck Institute for Biological Cybernetics. We also optimize the configurable cabin position of the simulator and assess the corresponding motion fidelity improvement. The software implementation of the method is publicly available.

Motion Fidelity Requirements for Helicopter-Ship Operations in Maritime Rotorcraft Flight Simulators

The research presented in this paper is part of a longer-term project to develop overall fidelity requirements for simulated helicopter shipboard operations to inform and support first-of-class flight trials. The paper reports the results of motion cueing assessment and optimization research, conducted in a six-degree-of-freedom motion flight simulator, to develop simulator motion drive laws capable of providing high-fidelity motion cueing for simulated shipboard operations. To do this, a novel objective technique, vestibular motion perception error (VMPE), has been developed. The technique was used to optimize the motion cues for simulated helicopter landings on a naval single-spot destroyer at different wind and sea-state conditions. New simulator motion tuning sets were derived offline and then tested experimentally to compare the objective VMPE predictions with subjective assessments from a test pilot. Results show the influence of different motion cues, airwake conditions, and ship motion states on the pilot’s overall perception of self-motion, control strategy, task performance, and workload. It was found that high-fidelity motion cueing becomes more desirable for the pilot at higher wind conditions and sea states, for which an “Optimized” motion setting was obtained using the new technique. Moreover, the use of an “Optimized” motion setting generated by the VMPE methodology resulted in reduced pilot workload, leading to improved simulated maritime helicopter operational capability. The technique provides a rational methodology for motion tuning, which could be applied in training and engineering simulators.

Increasing Motion Fidelity in Driving Simulators Using a Fuzzy-Based Washout Filter

The motion cueing algorithm is the procedure used to regenerate vehicle motion cues by transforming translational and rotational motions of a simulated vehicle into the simulator motion such that high fidelity motions can be generated through a washout filter. Classical washout filters are widely being used in different motion simulators because of their low computational load, simplicity, and functionality. However, they have a number of disadvantages that make them unreliable in some cases. One of the main disadvantages is its parameter selecting procedure, which is based on trial-and-error and the worst case driving scenario. In addition, the washout filter parameters are kept constant during different driving scenarios, causing inflexibility of the filter design. This causes conservative platform workspace usage and as a result false motion cues. Furthermore, the mathematical model of human motion sensation plays no role in designing of the classical washout filter. Ignorance of the online information about physical limitations of the platform and the simulator driver motion sensation factors are other main drawbacks of classical washout filters. The aim of this research is to provide a fuzzy logic-based washout filter that considers the motion sensation error between the real vehicle and simulator drivers as well as the distance of the simulator platform from its physical boundaries to correct false motion cues and decrease human sensation errors. The proposed fuzzy-based washout filter also seeks to increase motion fidelity and enable the simulator to use the platform workspace more efficiently. The simulation results show the efficiency of the proposed fuzzy-based washout filter in reducing the human sensation error and enhancing efficiency of the simulator platform workspace usage.

Space-time variant weighted regularization in compressed sensing cardiac cine MRI

PurposeTo analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methodsk-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. ResultsThe proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. ConclusionsUsage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.

A post-processing method based on interphase motion correction and averaging to improve image quality of 4D magnetic resonance imaging: a clinical feasibility study.

Nine patients (seven pancreas, one liver, and one lung) were recruited. 4D-MRI was performed using two prototype k-space sorted techniques, stack-of-stars (SOS) and koosh-ball (KB) acquisitions. Post-processing using MoCoAve was implemented for both methods. Image quality score, apparent SNR (aSNR), sharpness, motion trajectory and standard deviation (σ_GTV) of the gross tumor volumes were compared between original and MoCoAve image sets. All subjects successfully underwent 4D-MRI scans and MoCoAve was performed on all data sets. Significantly higher image quality scores (2.64 ± 0.39 vs 1.18 ± 0.34, p = 0.001) and aSNR (37.6 ± 15.3 vs 18.1 ± 5.7, p = 0.001) was observed in the MoCoAve images when compared to the original images. High correlation in tumor motion trajectories in the superoinferior direction (SI: 0.91 ± 0.08) and weaker in the anteroposterior (AP: 0.51 ± 0.44) and mediolateral (ML: 0.37 ± 0.23) directions, similar image sharpness (0.367 ± 0.068 vs 0.369 ± 0.072, p = 0.805), and minimal average absolute difference (0.47 ± 0.34 mm) of the motion trajectory profiles was found between the two image sets. The σ_GTV in pancreas patients was significantly (p = 0.039) lower in MoCoAve images (1.48 ± 1.35 cm3) than in the original images (2.17 ± 1.31 cm3). MoCoAve using interphase motion correction and averaging has shown promise as a post-processing method for improving k-space sorted (SOS and KB) 4D-MRI image quality in thoracic and abdominal cancer patients. The proposed method is an image based post-processing method that could be applied to many k-space sorted 4D-MRI methods for improved image quality and signal-to-noise ratio while preserving image sharpness and respiratory motion fidelity. It is a useful technique for the radiotherapy planning community who are interested in using 4D-MRI but aren't satisfied with their current MR image quality.

A particle swarm approach for tuning washout algorithms in vehicle simulators

The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms. These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fidelity indicators, so that the process can be automated. A genetic algorithm (GA) has been recently proposed to perform this process, with promising results. Following this approach, this paper proposes applying Particle Swarm Optimization (PSO) to solve the MCA tuning problem. The PSO-based proposed solution is assessed using the classical washout MCA, comparing its performance, convergence and correctness against the GA-based solution. Results show that a PSO-based tuning of MCA can provide better results and converges faster than a GA-based one. In addition, PSO is easier to set-up than GA, since only one parameter of the optimization algorithm itself (the number of particles) needs to be set-up, instead of a minimum of four in the case of the GA.

Simulación de Plataformas Robóticas de Movimiento para Aplicaciones de Realidad Virtual Mediante Filtros Digitales

Robotic motion platforms are used in many vehicle simulators and Virtual Reality applications. However, the set-up of the so-called washout algorithms that control the generation of self-motion is a hard process, since a great deal of tests need to be performed before reaching a proper motion fidelity. The availability of simulation tools eases this tuning task. Therefore, a motion platform characterization and simulation method is proposed in this paper. The method relies on second order digital filters and provides a reliable, yet very fast simulation system, which is assessed by means of a two degree-of-freedom motion platform, although the method might be applied to simulate other motion mechanisms.