Prolonged exposure to whole-body vibration (WBV) is a key contributor to motion discomfort in vehicles, including motion sickness and ride comfort. This issue becomes more compelling in automated vehicles, where occupants are expected to frequently engage in non-driving-related activities and will expect high comfort levels. Hence, enhancing seat design to mitigate WBV is essential for improving ride comfort across vehicle types. Therefore, this study, which primarily addresses vertical accelerations, optimized an existing seat suspension (K-Seat) and subjectively assessed discomfort using 24 participants (13 males and 11 females) exposed to a 29-minute driving session. The experiment was conducted with a conventional Toyota Yaris seat in a driving simulator, where a K-Seat model was used to emulate the effect of the seat suspension. Thus we evaluated the K-Seat, which has shown great promise for attenuating low-frequency vibrations; however, it had never been tested on human participants. The results show an overall reduction of 50% in reported motion sickness using the motion illness symptoms classification scale (MISC). Subjective discomfort was also alleviated for head and upper back. In addition, perceived discomfort was analyzed based on gender, illustrating a greater effectiveness of the K-Seat in enhancing lower neck comfort for females than for males.

The security of Automated Vehicles (AVs) is an important emerging area of research in traffic safety. Methods have been published and evaluated in experimental vehicles to secure safe AV control in the presence of attacks, but human motion comfort is rarely investigated in such studies. In this paper, we present an innovative optimal-coupling-observer-based framework that rejects the impact of bounded sensor attacks in a network of connected and automated vehicles from safety and comfort point of view. We demonstrate its performance in car following with cooperative adaptive cruise control for platoons with redundant distance and velocity sensors. The error dynamics are formulated as a Linear Time Variant (LTV) system, resulting in complex stability conditions that are investigated using a Linear Matrix Inequality (LMI) approach guaranteeing global asymptotic stability. We prove the capability of the framework to secure occupants’ safety and comfort in the presence of bounded attacks. In the onset of attack, the framework rapidly detects attacked sensors and switches to the most reliable observer eliminating attacked sensors, even with modest attack magnitudes. Without our proposed method, severe (but bounded) attacks result in collisions and major discomfort. With our method, attacks had negligible effects on motion comfort evaluated using ISO-2631 Ride Comfort and Motion Sickness indexes. The results pave the path to bring comfort to the forefront of AVs security.

We present a vehicle system capable of navigating safely and efficiently around Vulnerable Road Users (VRUs), such as pedestrians and cyclists. The system comprises key modules for environment perception, localization and mapping, motion planning, and control, integrated into a prototype vehicle. A key innovation is a motion planner based on Topology-driven Model Predictive Control (T-MPC). The guidance layer generates multiple trajectories in parallel, each representing a distinct strategy for obstacle avoidance or non-passing. The underlying trajectory optimization constrains the joint probability of collision with VRUs under generic uncertainties. To address extraordinary situations ('edge cases') that go beyond the autonomous capabilities - such as construction zones or encounters with emergency responders - the system includes an option for remote human operation, supported by visual and haptic guidance. In simulation, our motion planner outperforms three baseline approaches in terms of safety and efficiency. We also demonstrate the full system in prototype vehicle tests on a closed track, both in autonomous and remotely operated modes.

4D-scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D-scans utilizing multiple asynchronous cameras encounters two main challenges: 1) finding dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D-shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. Here, we introduce a generic framework able to 1) find and align dynamic features in the 3D-scans captured by each camera using the nonrigid-iterative-closest-farthestpoints algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTMbased-network capable of aligning 3D-scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a highquality 3D-mesh model using a non-rigid registration method. With a newly developed 4D-foot-scanner, we validate the framework and create the first open-access data-set, namely the 4D-feet. It includes 4Dshapes (15 fps) of the right and left feet of 58 participants (116 feet including 5147 3D-frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D-scans.

Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.

Fitting apparel and apparel in performing different activities is essential for the functional yet comfortable experience of the user. 4D scans, i.e. 3D scans in continuous timestamps, of the body (part) in performing those activities are the basis for the design of garments/apparel in 4D. In this paper, we proposed a semi-automatic workflow for constructing 4D scans of the body parts with the emphasis on registering noisy scans at a given timestamp. Continuous 3D scans regarding the moving body parts are captured first from different depth cameras from different view angles. In a given timestamp, the collected 3D scans are roughly aligned to a template using the rigid Iterative Closest Points (ICP) algorithm. Then these scans are further registered using a newly proposed non-rigid Iterative Closest-Farthest Points (ICFP) algorithm, in which correspondences between the source and the target are established by either closest or farthest points based on the newly defined logical distance concept and the probability theory. Experimental results indicated that the ICFP method is robust against noise and the scanning accuracy can be as high as 3.4 %. It also reveals that, for the human foot, the differences of ball width and ball angles between the loaded and the unloaded situation can be as large as 8 mm and 2 degrees, respectively. This highlights the importance of using 4D scan in designing garments and apparel.

Optical motion capturing explains the three-Dimensional (3D) position estimation of points through triangulation employing several depth cameras. Prosperous performance relies on level of visibility of points from different cameras and the overlap of captured meshes in-between. Generally, the accuracy of the estimation is practically based on the camera parameters e.g., location and orientations. Accordingly, the camera network configurations play a key role in the quality of the estimated mesh. This paper proposes an optimal approach for camera placement based on characteristics of a depth camera D435i - Intel RealSense. The optimal problem includes a cost function that contains several minimisation and maximisation terms. The minimisation terms are distance of the cameras to the center of the scanning object, resolution error, and sparsity. And the maximisation terms are distance between each two pair of cameras, percent of captured point from an object, and the level of overlap between cameras. The object is designed based on practical experiments of human walking and is a bounding box around one step of dynamic foot work-space from heel strike posture to toe-off posture. The accuracy and robustness of the algorithms are assessed via experiment measurement, and sensitivity to the number of cameras is investigated. Accordingly, the experiment results determined that the scanning accuracy can be as high as 2.5 % based on a reference scan with a high-end scanner (Artec Eva).

Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient’s initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.Please check and confirm if the author names and initials are correct for “Yongjie Jessica Zhang” and “Wing Kam Liu”.We confirm they are correct.

A high-fidelity digital representation of (part of) the human body is a key enabler for integrating humans in a digital twin. Among different parts of human body, building the model of the hand can be a challenging task due to the posture deviations among collected scans. In this article, we proposed a posture invariant statistical shape model (SSM) of the human hand based on 59 3D scans of human hands. First, the 3D scans were spatially aligned using a Möbius sphere-based algorithm. An articulated skeleton, which contains 20 bone segments and 16 joints, was embedded for each 3D scan. Then, all scans were aligned to the same posture using the skeleton and the linear blend skinning (LBS) algorithm. Three methods, i.e., principal component analysis (PCA), kernel-PCA (KPCA) with different kernel functions, and independent component analysis (ICA), were evaluated in the construction of the SSMs regarding the compactness, the generalization ability, and the specificity. The PCA-based SSM was selected, where 20 principal components were used as parameters for the model. Results of the leave-one-out validation indicate that the proposed model was able to fit a given 3D scan of the human hand at an accuracy of 1.21 ± 0.14 mm. Experiment results also indicated that the proposed SSM outperforms the SSM that was built on the scans without posture correction. It is concluded that the proposed posture correction approach can effectively improve the accuracy of the hand SSM and therefore enables its wide usage in human-integrated digital twin applications.

Sophisticated surgeons are widely indicating the use of surgical robots in order to reject human error, increase precision, and speed. Among well-known robotic mechanisms, parallel robots are broadly more investigated regarding their special characters as higher acceleration, speed, and accuracy, and less weight. Specific surgical procedures confine and restrict their workspace, while controlling and validating the robots are complicated based on their complex dynamic. To this end, in this paper, a 6-DOF robot, with linear manipulators, is designed and controlled. Addressing the inherent nonlinearity of the robot mechanism, an adaptive PID manipulator is employed and validated with nonlinear model. Thus, this paper aims to implement the controller using MATLAB on the nonlinear model designed in Adams software as online. Furthermore, as feasibility study, a 3-axis gyro sensor is calibrated and used to produce complex real movements and desires, which is sending real time signals to MATLAB with microcontroller ATmega 2560. Simulation results show that the adaptive controller identifies the system's dynamic and proceed to reducing error of the position by the time. In addition, the method defines the workspace of both the states and forces which can be an introduction to comprehensive design of such robots.

Sophisticated surgeons are widely indicating the use of surgical robots in order to reject human error, increase precision, and speed. Among well-known robotic mechanisms, parallel robots are broadly more investigated regarding their special characters as higher acceleration, speed, and accuracy, and less weight. Specific surgical procedures confine, and restrict their workspace, while controlling and validating the robots are complicated regarding to their complex dynamic. To this end, in this paper, a 6-DOF robot, with rotary manipulators, is designed and controlled. Addressing nonlinearity of parallel robots, an innovative methodology is formulated to robustly penalize the error of tracking at end effector through a Linear Quadratic Integral (LQI) regulator with online Artificial Neural Network (ANN) gain tuning, based on non-linear model in format of a Linear Time Invariant (LTI) model. As validation, the controller is implemented using MATLAB on the non-linear model designed in Adams software online. Simulation results demonstrate the optimal controller penalizing the error while minimizing torque on each of the rotary manipulator. In addition, the method defines the workspace of both the states and torques, which is an introduction to comprehensive design of such robots.