Accurate Human Body Model Research Articles

Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

Using video sequences to restore 3D human poses is of great significance in the field of motion capture. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The model is pre-trained using open-source human pose datasets; (2) Human-body pose generation based on video streams. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements. Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data.

2D 이미지 기반 형상 변형을 이용한 개인 맞춤형 인체 하반신 모델링

A realistic and accurate human body model is being used as a powerful tool for designing for a human-centered product as an international trend in ergonomics, game industry, and automobile industry. In this respect, there have been many efforts and studies to obtain the realistic and accurate human body model. But there are limitations on each type of data required to make this model. Therefore, this study proposes a novel method for creating a personalized human body models using two 2D images and a human template model. When the template model is determined through simple individual information such as the user’s height and weight, the personalized human body model is created by the following four steps; i) Image processing and silhouette extraction, ii) Defining characteristic points and creating key-sections, iii) Width deformation through matching with body silhouette, and iv) Creating a sweep-based human body model. Unlike existing methods of generating the human body models through 3D scan data, the proposed method can provide convenience to the user by requesting simple information. In addition, the method can improve accuracy of the model through deforming the width by matching with the body silhouette. Finally, the study demonstrated the validity of the method by comparing the result model generated by the purposed method with the model created through the 3D scan data.

HBM tester waveforms, equivalent circuits, and socket capacitance

The Tektronix CT2 current probe is used to acquire more accurate Human Body Model waveforms with 0Ω and 500Ω tester loads than a CT1, owing to the CT2’s low-frequency performance. The integrals and centroids of these waveforms then readily yield precise values of tester circuit elements and effective socket capacitance. Expressions are derived for effective socket capacitance resulting from distributed capacitance along a transmission line connecting to an unmatched load. These lead to options for reducing the effective socket capacitance while retaining the lines for delivering the HBM pulse.

Foreword by the Editor [EM Programmer's Notebook

Voltage and power transfer functions for the path loss of a 402 MHz body-area network are reviewed. The corresponding expressions are valid in both the near- and far-fields of a transmitting antenna. It was shown that basic FDTD simulations for a homogeneous human-body model, implemented in MATLAI3®, agreed quite well with the advanced FEM solver for an inhomogeneous accurate human-body model. Both methods modeled a voltage transfer function (path loss) for out-of-body dipole antennas at 402 MHz at different antenna locations, as close to the body as 15 mm. The reason for the good agreement was that the propagation path was mostly determined by a diffraction of the electromagnetic signal around the body, and not by propagation through the (inhomogeneous) body. Such an observation made it possible to use various homogeneous body meshes in order to study the effect of different body types and positions for out-of- body antennas. A method of creating such meshes using a three-dimensional body scanner is described. For a number of different white-male body meshes, the magnitudes of the received voltages matched exceptionally well when the antenna positions were measured from the top of the head.

Modeling Accuracy and Features of Body-Area Networks with Out-of-Body Antennas at 402 MHz

Voltage and power transfer functions for the path loss of a 402 MHz body-area network are reviewed. The corresponding expressions are valid in both the near- and far-fields of a transmitting antenna. It was shown that basic FDTD simulations for a homogeneous human-body model, implemented in MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , agreed quite well with the advanced FEM solver for an inhomogeneous accurate human-body model. Both methods modeled a voltage transfer function (path loss) for out-of-body dipole antennas at 402 MHz at different antenna locations, as close to the body as 15 mm. The reason for the good agreement was that the propagation path was mostly determined by a diffraction of the electromagnetic signal around the body, and not by propagation through the (inhomogeneous) body. Such an observation made it possible to use various homogeneous body meshes in order to study the effect of different body types and positions for out-of-body antennas. A method of creating such meshes using a three-dimensional body scanner is described. For a number of different white-male body meshes, the magnitudes of the received voltages matched exceptionally well when the antenna positions were measured from the top of the head.

MRI‐phased array evaluation using a human body model

AbstractWe develop methodology to evaluate MRI‐phased array receivers using an anatomically and electromagnetically accurate human body model. The human body model accurately represents the different permittivity and conductivity values of various tissue types in the human body, at the frequency of interest. The phased array receiver is a posterior array with 16 coils, typically placed on the patient table for imaging. We obtain the electric and magnetic fields associated with each coil using the finite‐difference time‐domain method and estimate a simplified metric to evaluate the receiver array contribution to the signal‐to‐noise ratio (SNR) distribution. The results highlight SNR variation in the left–right direction, an artifact seen in imaging, but not apparent in quasi‐static simulations with uniform phantoms. We use the results to compare two design variations of 16‐coil phased arrays. In addition, we use the results to compare imaging methods that have potential to reduce the computational complexity of high channel count arrays by reducing the number of data streams used in image reconstruction. © 2010 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 37B: 20–28, 2010