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Accurate anthropometric measurements are critical in the healthcare industry for effective diagnosis and patient monitoring. Standardized procedures for measuring human body dimensions are required to ensure accuracy, reliability, and consistency across measurement data. Traditional body measurement administered by trained physicians is susceptible to human bias and often requires direct physical contact with the patient which can be intrusive. Other measurement systems have been introduced to automate the process and reduce reliance on physicians, but they tend to be expensive and complicated to set up and operate. This study aims to develop a cost-effective method for measuring anthropometric parameters using a single RGBD camera designed for easy deployment and low computing resources. The system only requires the participant to rotate a full 360-degree in front of the camera to reconstruct a detailed 3D human body model based on pose graph reconstruction. A study involving 20 participants is conducted to evaluate the accuracy of the proposed system as compared to traditional measurement using a tape measure. The result shows a height percentage error of 0.55 %, while hip and waist measurements show an error of 3.84 % and 6.90 %, respectively. The integration of the ellipse shrinkage method into the algorithm effectively improves hip and waist measurements by minimizing the impact of clothing.

In recent years, remarkable strides have been made in 3D human reconstruction. However, the focus has primarily been on pose reconstruction, which fails to capture the true shape of the human body, particularly for individuals who are overweight or underweight. Therefore, based on a combination of regression and optimization, as well as ensuring accurate posture, we incorporate reasonable prior knowledge for the shape part of the reconstructions during iteration to obtain a Skinned Multi-Person Linear Model (SMPL) with a reasonable shape. During the training process, in regard to the pose, we fit 2D joint points, which can be obtained through images, and the training is still feasible even if the ground truth is missing; for the shape, we obtain the dimension information by measurement, but the initial height and waist need to be provided. To assess our method, we collect the dimension data from 200 individuals and achieved a remarkable average precision of 97 % based on a thorough comparison of the results. Our main research objective is to construct a more realistic model of the human body by adding reasonable body dimension information as prior knowledge during the iterative loop, and to measure 38 body dimensions of the human body. Our experiments demonstrate that our method, in regard to the shape, outperforms other state-of-the-art techniques while simultaneously maintaining postural accuracy. [ABSTRACT FROM AUTHOR]

We present a camera‐based human body parameters measurement approach and develop a human postural assessment system. The approach combines the conventional contact measurement method and the non‐contact measurement method to overcome some shortcomings in terms of time, expense, and professionalism in early methods. The entire measurement system consists of a computer, a high‐definition camera, and the sticky points that are applied to the participant's body before the measurement. The camera captures the triple view image of human body. Then, the human body outline and the joint points of the human skeleton are extracted to locate the bone feature points. Finally, measurements and extractions of the human parameters are made. Experimental results demonstrate that the global postural assessment system provides quantitative guidance for human postural evaluation, and it completely changes how human postural is evaluated. The postural assessment system is significant for early diagnosis of diseases and medical rehabilitation treatment. [ABSTRACT FROM AUTHOR]

The shape and pose estimation of a human body are essential for human behavior analysis, sports and medical analysis, and virtual reality. Although 2D image data are much easier to acquire than 3D scan data, the estimation accuracy using 2D images is still far below the 3D scanning methods’ ones. In this paper, we propose a 2D image-based human body estimation method appropriate for body shape and size measurement. The proposed method uses SMPL (Skinned Multi-Person Linear model), a human body model, and rich correspondences of Dense-Pose network so named Dense-Pose2SMPL, which estimates the SMPL parameters estimation by minimizing the re-projection error of the correspondences between the pixels in the human image and 3D surface points in SMPL. The previous SMPL parameter estimation methods use the sparse joint correspondence so that they show very limited performance in shape estimation. We compare the body measurement accuracy of Dense-Pose2SMPL with a single human image with SMPLify, a joint-based estimation method, and DecoMR, a Dense-Pose-based neural network regression method. The experiments results show a dramatic improvement of Dense-Pose2SMP over SMPLify and DecoMR. In the circumference estimation error, over 30 percent decrease for the overweight and underweight and 10 to 20 percent decrease on average. We also provide an analysis of the effects of the input conditions: the subject’s BMI level, pose, clothing style, and camera viewpoint. A-pose, side (profile) camera views and minimal and tight clothing style show higher accuracy than other conditions. Finally, the extension with multiple (two) images can give around 15 percent more improvement in body size estimation accuracy over the single image-based method.

Introduction: Design of next-generation ambulance patient compartment requires up-to date anthropometric data of emergency medical service providers (EMSP). Currently, no such data exist in the U.S. A large-scale anthropometric study of EMSP in the U.S. were conducted. This report provided the summary statistics (means, standard deviation, and percentiles) of the study's results and examined the anthropometric differences between the EMSP dataset and the U.S. general population, and between the EMSP dataset and U.S. military personnel dataset, respectively. Method: An anthropometric study of 471 male and 161 female EMSP from across the continental US was conducted, using a sampling strategy that took into account age, sex, and race strata. Results: On average, male EMSP were found to be 18 mm taller and 7 kg heavier than US male general population, and 19 mm taller and 11 kg heavier than US male military personnel. Female EMSP were found to be 25 mm taller than US female general population, and 10 kg heavier than US female military personnel. Conclusions: These results showed that it would be inappropriate to apply general population or military data to the design of next-generation ambulance patient compartment. This new dataset provided the most recent and accurate EMSP anthropometric measurements available in the US. Practical Application: Data from this study provided an invaluable resource for the design of next-generation ambulances in the US. [ABSTRACT FROM AUTHOR]

Anthropometric parameter based human modeling has been attractively explored and applied in human body related product design industries. However, how to conveniently obtain precise anthropometric parameters and how to make the human modeling results fit the anthropometric parameters well are still unsolved problems. In this paper, a smart garment attached with a mechatronic sensor-net has been designed to measure multiple body dimensions precisely and simultaneously. Geometrically, this sensor-net corresponds to a feature curve-net. A 3D human model is statistically reconstructed with body measurements initially and is further optimized with the shape constraints from the feature curve-net. Experimental results verify the effectiveness of our approach in both human body measurement and 3D human body modeling. [ABSTRACT FROM AUTHOR]

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Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods.

Non-contact human body measurement plays an important role in surveillance, physical healthcare, on-line business and virtual fitting. Current methods for measuring the human body without physical contact usually cannot handle humans wearing clothes, which limits their applicability in public environments. In this paper, we propose an effective solution that can measure accurate parameters of the human body with large-scale motion from a Kinect sensor, assuming that the people are wearing clothes. Because motion can drive clothes attached to the human body loosely or tightly, we adopt a space-time analysis to mine the information across the posture variations. Using this information, we recover the human body, regardless of the effect of clothes, and measure the human body parameters accurately. Experimental results show that our system can perform more accurate parameter estimation on the human body than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Measurement of human body is useful for ergonomic design in manufacturing. We aim to accurately measure the shapes of human feet for the design of shoes, for which measuring the dynamic shape of the foot in motion is important because the foot deforms while walking or running. In this paper, we propose a method for measuring the anatomical feature cross-sections of the foot while walking. The dynamic shape of feature cross-section has never been measured, though those features are very basic and popular. Our proposed method is based on the multi-view stereo method. The target cross-sections are painted in individual colors (red, green, yellow and blue). Several nonlinear conditions are introduced in the process to find the consistent correspondence in all images. We show that the proposed method for dynamic measurement achieved the desired accuracy similar to existing 3D static foot scanners. [ABSTRACT FROM AUTHOR]