Wireless Capsule Endoscope Localization with Phase Detection Algorithm and Adaptive Body Model.

Safety of children in cars: A review of biomechanical aspects and human body models

In this paper, we develop a system for automated human body tracking and modeling based on a monocular camera. In this system, ten body parts including head, torso, arms and legs are extracted to build a 2D human body model. One way to decompose human silhouette into different parts is to generate cuts between the negative minimum curvature (NMC) points. However, due to the self-occlusion problem and left-right ambiguity, each individual body part cannot be successfully identified in every frame. Therefore, in addition to utilizing the NMC points, we design a forward and backward tracking mechanism to identify the location of head in each frame. The torso angle and size are determined by integrating multiple-frame information with the modified solution of Poisson equation. Hands and feet can then be identified correctly based on a modified star skeleton approach along with the nearest-neighbor tracking mechanism. The rest of joint points can also be located by making use of the notion “connectivity”. In the experiments, we analyze the performance of the proposed human body modeling mechanism. We also demonstrate a behavior analysis application by employing the proposed method. The experiment results verify the robustness of the proposed approach and the feasibility of the employing the proposed approach to the action recognition application.

Physical-layer characterization is important for design of in-to-out body communication for wireless body area networks (WBANs). This paper numerically investigates the path loss and absorption of an in-to-out body radio frequency (RF) wireless link between an endoscopy capsule and a receiver outside the body using a 3D electromagnetic solver. A spiral antenna in the endoscopy capsule is tuned to operate in the Medical Implant Communication Service (MICS) band at 402 MHz, accounting for the properties of the human body. The influence of misalignment, rotation of the capsule, and three different human models are investigated. Semi-empirical path loss models for various homogeneous tissues and 3D realistic human body models are provided for manufacturers to evaluate the performance of in-body to out-body WBAN systems. The specific absorption rate (SAR) in homogeneous and heterogeneous body models is characterized and compliance is investigated.

Power efficient ultra-wideband based wireless communication for wireless body area network applications

Recent advances in compressed sensing (CS) enable accurate CT image reconstruction from highly undersampled and noisy projection measurements, due to the sparsifiable feature of most CT images using total variation (TV). These novel reconstruction methods have demonstrated advantages in clinical applications where radiation dose reduction is critical, such as onboard cone-beam CT (CBCT) imaging in radiation therapy. The image reconstruction using CS is formulated as either a constrained problem to minimize the TV objective within a small and fixed data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning, which significantly limit the clinical use of CS-based iterative reconstruction. In this paper, we propose an optimization algorithm for CS reconstruction which overcomes the above two drawbacks. The data fidelity tolerance of CS reconstruction can be well estimated based on the measured data, as most of the projection errors are from Poisson noise after effective data correction for scatter and beam-hardening effects. We therefore adopt the TV optimization framework with a data fidelity constraint. To accelerate the convergence, we first convert such a constrained optimization using a logarithmic barrier method into a form similar to that of the conventional TV regularization based reconstruction but with an automatically adjusted penalty weight. The problem is then solved efficiently by gradient projection with an adaptive Barzilai-Borwein step-size selection scheme. The proposed algorithm is referred to as accelerated barrier optimization for CS (ABOCS), and evaluated using both digital and physical phantom studies. ABOCS directly estimates the data fidelity tolerance from the raw projection data. Therefore, as demonstrated in both digital Shepp-Logan and physical head phantom studies, consistent reconstruction performances are achieved using the same algorithm parameters on scans with different noise levels and∕or on different objects. On the contrary, the penalty weight in a TV regularization based method needs to be fine-tuned in a large range (up to seven times) to maintain the reconstructed image quality. The improvement of ABOCS on computational efficiency is demonstrated in the comparisons with adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS), an existing CS reconstruction algorithm also using constrained optimization. ASD-POCS alternatively minimizes the TV objective using adaptive steepest descent (ASD) and the data fidelity error using projection onto convex sets (POCS). For similar image qualities of the Shepp-Logan phantom, ABOCS requires less computation time than ASD-POCS in MATLAB by more than one order of magnitude. We propose ABOCS for CBCT reconstruction. As compared to other published CS-based algorithms, our method has attractive features of fast convergence and consistent parameter settings for different datasets. These advantages have been demonstrated on phantom studies.

Wireless capsule endoscope (WCE) localization is critical for targeted drug delivery and precise diagnosis of gastrointestinal (GI) lesions. However, the precise location and reliable tracking of a WCE within the complex anatomical environment of the human body remain a significant challenge. This study investigates an AI-integrated wearable antenna system for WCE localization in heterogeneous environments. Experimental validation is conducted using a realistic multilayer heterogeneous phantom (MHP) that replicates human tissue properties, comprising distinct skin, fat, and muscle layers, with an inner cavity filled with GI tract-mimicking saline solution. The proposed system employs a conformal capsule transmitter and four wearable receiver antennas operating at 2.45 GHz in the industrial, scientific, and medical band, which are interfaced with software-defined radios to collect received signal strength (RSS) data. The measurement setup enabled the collection of 3,300 RSS data points, which were systematically mapped to determine the capsule’s position. Data-driven frameworks employing five machine learning regression models: k-nearest neighbors (KNN), adaptive boosting (AdaBoost), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), were utilized to enhance localization accuracy. Among these, XGBoost demonstrated the highest localization accuracy, with a remarkable root-mean-square error of 1.69 cm for 3D localization and 0.39 cm for trajectory tracking within the MHP. These findings highlighted the potential of the proposed AI-integrated wearable antenna system for localization and precise real-time capsule tracking, thereby advancing WCE-based diagnostics and treatment strategies.

Acquisition of various human body models is useful in many cases. In this paper, we present some of our recent work on body creation tools. Our goal is to enable rapid creation of various body models that are immediately usable for animation. In doing so, we aim to carry out realistic deformations on the human body models as well as make its usage simple. Our system is composed of several modules: (1) Skin attachment to an H-Anim skeleton is carried out first in order to get deformation in skeletal shape modification as well as in animation. (2) Volumetric deformation module deals with the volumetric scale of body parts such as breast, belly and bottoms. These deformation operators, together with the skeletal deformation allow the automatic adaptation of the body model to different sizes and proportions to accommodate anthropometrical variations. (3) Surface optimization is used to simplify the model in consideration of not only geometric features but also the animation aspect of it. (4) Finally, the BDP generation module describes the geometry of the model as well as how to animate it according to the MPEG-4 BDP specifications.Key wordsDeformationsimplificationMPEG-4 BDPseamless bodyanimation ready model.

In the paper authors present a concept and a modelling method of the static and dynamic loads of human body’s parts: bones and muscles during movement. Currently the problem of human body modelling is very important for many domains of our “better life” programs e.g.: an automotive—to find the best solution for human protection during accidents, sport—to find the most efficient and least tiring movements, to the health protection or extend the active life of the elderly. The human body is not a rigid multi-body system, but elastic, flexible and varying according to time. It consists of semi-stiff bones, elastic muscles and tendons. Other parts like stomach or liver are hanging on elastic wires and move relatively to each other during the whole acceleration process etc. All those elements can be broken or fatigued under some load. So the model of human body is not linear and shouldn’t be modelled by linear equation sets. Authors present the concept of human body modelling based on three types element chain. One type are “bones” that are coupled in joints and conduct loads to the support surface; the elastic tendons that keep the joints and propel bones rotation in joints, and muscles that generate forces for stabilization system or for propelling the bones. In paper the simplified model of human body built in MATLAB/Simulink software is presented. Some results of simulation e.g. load of a knee during squat or landing after jump are compared with real test results.

The accuracy of electromagnetic (EM) exposure assessments depends mainly on the resolution of a voxel human body model. The resolution of the conventional human body model is limited to a few millimeters. In the millimeter wave (mmWave) frequency range, EM waves are absorbed by the superficial tissues in the human body. Therefore, resolution and skin thickness of the human body model are important for accuracy of the EM wave exposure metrics recommended by international human safety guidelines. Realistic thickness modeling of the skin tissue on the human body may provide greater accuracy in the EM exposure assessment, especially at mmWave frequency range. In this paper, effects of the skin thickness on the EM exposure metrics in one-dimensional multi-layered models obtained from different regions of the body model are investigated using the dispersive algorithm based on the finite-difference time-domain method over the frequency range from 1 to 100 GHz. Furthermore, effects of eyelid tissue in a human eye on the EM exposure metrics are studied over the frequency range. The EM exposure metrics such as absorbed power density, heating factor, and power transmission coefficient are calculated up to 100 GHz to evaluate the limits of EM wave exposure.

Parametric 3D modeling of young women's lower bodies based on shape classification

ABSTRACTObjective: Computational human body models (HBMs) are nominally omnidirectional surrogates given their structural basis in human anatomy. As a result, such models are well suited for studies related to occupant safety in anticipated highly automated vehicles (HAVs). We utilize a well-validated HBM to study the head and neck kinematics in simulations of nontraditional occupant seating configurations.Methods: The GHBMC M50-O v. 4.4 HBM was gravity settled into a generic seat buck and situated in a seated posture. The model was simulated in angular increments of 15 degrees clockwise from forward facing to rear facing. A pulse of 17.0 kph (NASS median) was used in each to simulate a frontal impact for each of the 13 seating configurations. Belt anchor points were rotated with the seat; the airbag was appropriately powered based on delta-V, and was not used in rear-facing orientations. Neck forces and moments were calculated.Results: The 30-degree oblique case was found to result in the maximum neck load and sagittal moment, and thus Neck Injury Criteria (NIJ). Neck loads were minimized in the rear facing condition. The moments and loads, however, were greatest in the lateral seating configuration for these frontal crash simulations.Conclusions: In a recent policy statement on HAVs, the NHTSA indicated that vehicle manufacturers will be expected to provide countermeasures that will fully protect occupants given any planned seating or interior configurations. Furthermore, the agency indicated that virtual tests using human models could be used to demonstrate such efficacy. While the results presented are only appropriate for comparison within this study, they do indicate that human models provide reasonable biomechanical data for nontraditional occupant seating arrangements.

In order to realize the accurate matching of 3D garment model and human model, a new method for feature extraction of garment and human model is proposed. We first construct local mesh fitting operator based on Voronoi graph and weighted parametric surface. Secondly, on the basis of the use of geometric attributes, the change in the neighborhood is used as the reference value. On this basis, the region is divided and then the feature point extraction of costume model and human body model is realized. Thirdly, a robust feature matching function is established. Finally, the clothing and the human body model can be matched quickly and accurately.

In this study, a simplified, parametric vehicle front structure was proposed to represent the real vehicle when impacted with full-scale finite element pedestrian human body model (HBM). To capture the real impact responses of human lower limbs, the real vehicle energy-absorbing structures were modeled using distributed beam elements and deformable shell elements to replicate the contact characteristics between vehicle and HBM. An investigation of vehicle front-end profile characteristics in worldwide popular sedan models was conducted to determine the ranges of geometry variables. A local stiffness measurement approach is also proposed. The simplified model is further validated using a detailed sedan model, and the impact responses of HBM in the two simulations correlate quite well with each other. Therefore it can be further used in the DOE study or optimization work in the vehicle front structure design for pedestrian lower limb impact protection.KeywordsVehicle front structureSimplified parametric modelLocal deformationHuman body model (HBM)Pedestrian impact

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.

In this paper, a 3D human body meshed model with low resolution is obtained by parametric digital human modeling, but the meshed surface is not smooth enough to meet the requirements of practical application. To achieve the smoothing effect of the geometric model of digital human body, this paper proposes a mesh-smoothing method of 3D human model based on Sqrt3 subdivision algorithm. Experiment shows that this method applies to the multicurvature mesh body model based on parametric modeling method and achieves good results. The method is simple, efficient and of well-reserved feature.