Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA. We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.

Department of Computer Science, Shahed University, Tehran, Iran Email: faeze.gholamrezaie@shahed.ac.ir Abstract The global fashion industry plays a pivotal role in the global economy, and addressing fundamental issues within the industry is crucial for developing innovative solutions. One of the most pressing challenges in the fashion industry is the mismatch between body shapes and the garments of individuals they purchase. This issue is particularly prevalent among individuals with non-ideal body shapes, exacerbating the challenges faced. Considering inter-individual variability in body shapes is essential for designing and producing garments that are widely accepted by consumers. Traditional methods for determining human body shape are limited due to their low accuracy, high costs, and time-consuming nature. New approaches, utilizing digital imaging and deep neural networks (DNN), have been introduced to identify human body shape. In this study, the Style4BodyShape dataset is used for classifying body shapes into five categories: Rectangle, Triangle, Inverted Triangle, Hourglass, and Apple. In this paper, the body shape segmentation of a person is extracted from the image, disregarding the surroundings and background. Then, Various pre-trained models, such as ResNet18, ResNet34, ResNet50, VGG16, VGG19, and Inception v3, are used to classify the segmentation results. Among these pre-trained models, the Inception V3 model demonstrates superior performance regarding f1-score evaluation metric and accuracy compared to the other models.

Although there is not an ideal biotype for all production systems, the adequate biotype should be determined according to the objectives that have been established for the herd, along with the production system being practiced [9]. This is not without consequences. For instance, larger animals usually have higher nutritional and general maintenance requirements [7]. Among the methods used to evaluate beef cattle, the EPMURAS methodology synthesized by Koury Filho [11], Koury Filho et al. [13] is one of the most utilized in Brazil. It consists in a visual assessment of body structure, precocity, muscularity, sheath, racial aspects, angulation and sexuality.

This paper presents Shape of You (SoY), an approach to improve the accuracy of 3D body shape estimation for vision-based clothing recommendation systems. While existing methods have successfully estimated 3D poses, there remains a lack of work in precise shape estimation, particularly for diverse human bodies. To address this gap, we propose two loss functions that can be readily integrated into parametric 3D human reconstruction pipelines. Additionally, we propose a test-time optimization routine that further improves quality. Our method improves over the recent SHAPY method by 17.7% on the challenging SSP-3D dataset. We consider our work to be a step towards a more accurate 3D shape estimation system that works reliably on diverse body types and holds promise for practical applications in the fashion industry.

We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes. ABS is based on the skinned multiperson linear (SMPL) body model, and aims to maximize BMnet measurement prediction error with respect to latent SMPL shape parameters. ABS is fully differentiable with respect to these parameters, and trained end-to-end via backpropagation with BMnet in the loop. Experiments show that ABS effectively discovers adversarial examples, such as bodies with extreme body mass indices (BMI), consistent with the rarity of extreme-BMI bodies in BMnet's training set. Thus ABS is able to reveal gaps in training data and potential failures in predicting under-represented body shapes. Results show that training BMnet with ABS improves measurement prediction accuracy on real bodies by up to 10%, when compared to no augmentation or random body shape sampling. Furthermore, our method significantly outperforms SOTA measurement estimation methods by as much as 3x. Finally, we release BodyM, the first challenging, large-scale dataset of photo silhouettes and body measurements of real human subjects, to further promote research in this area. Project website: https://adversarialbodysim.github.io

What the marketing world witnessed in April 2020, was an unprecedented rise of eCommerce retail sales in North America by triple digits. And, Statista reports that in the same 2020, more than two billion people bought their products from eCommerce stores, with global e-retail sales of over $4.2 trillion. While this is big news for eCommerce stores, an unfortunate incident is that the amount of returns purchasers make may go over a trillion dollars a year if care is not taken. The main reason for this is that most eCommerce shops have not been able to create size charts, so we have sizing problems plaguing e-commerce stores in a lot of ways. The basis of sizing issues eCommerce stores face is that they are outlets and usually get their products from different manufacturers across the globe.

Short men and obese women earn up to $1,000 (£700) less per year than their taller, skinnier counterparts, according to a new study into body shape and salary. This is evidence of a long suspected'beauty premium' that suggests physical attractiveness demands a higher value in the labour market, according to lead author Suyong Song from the University of Iowa. Researchers examined data from 2,383 volunteers, including whole body scans and information on their family income and gender. They found that in men earning over $70,000 (£50,000) per year, a centimetre increase in height was worth $1,000 (£700) extra in income per year. For women earning the same amount, every single point decrease in BMI was worth an extra $1,000 (£700) per year in their pay cheque, the researchers discovered.

The Lyfsize application is a revolutionary new way to measure your body sizes through the use of Artificial Intelligence. The app instantly gives you precise full body measurements through 3D body scanning from just two smartphone pictures. All you need to do is have someone click your photos and all your measurements are accurately calculated from them. You can share these measurements with designers to have custom clothing made for you. You can also use the measurements to find out your size in different clothing brands.

The use of artificial intelligence has been of enormous economic benefit for dairy farmers in many countries through the improvement of their stock. Affordable tools with the ability to continuously monitor the growth rate of livestock animals are highly sought after by the livestock industries. This demand is driven by the potential for these tools to assist in improving animal welfare and production efficiency. In a rapidly growing population, the demand for meat is escalating, especially in Asia, where the middle class is currently expanding. Meanwhile, in the western world there is growing consumer concern surrounding animal husbandry, with certain organisations labelling some of the current husbandry practices cruel or sub-standard.

Uniquely identifying individuals using anthropometric and gait data allows for passive biometric systems, where cooperation from the subjects being identified is not required. In this paper, we report on experiments using a novel data set composed of 140 individuals walking in front of a Microsoft Kinect sensor. We provide a methodology to extract anthropometric and gait features from this data and show results of applying different machine learning algorithms on subject identification tasks. Focusing on KNN classifiers, we discuss how accuracy varies in different settings, including number of individuals in a gallery, types of attributes used and number of considered neighbors. Finally, we compare the obtained results with other results in the literature, showing that our approach has comparable accuracy for large galleries.