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We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art approaches for body measurement from point clouds and images, demonstrate the comparable performance with the best methods, and even outperform several deep learning models on public datasets. The simplicity of the proposed regression model makes it perfectly suitable as a baseline in addition to the convenience for applications such as the virtual try-on. To improve the repeatability of the results of our baseline and the competing methods, we provide guidelines toward standardized body measurement estimation.

The study aims to determine the relationship between body weight and linear body measurement traits and to establish a model to predict body weight from linear body measurement traits using MARS and CART. A total of 426 Nguni goats (Male = 69 and female = 357) aged between one to four years were used for body weight (BW) and some linear body measurements such as heart girth (HG), body length (BL) and withers height (WH). The average body weight of male and female goats was 32.50 and 33.02 kg, respectively. MARS and CART were used to predict BW from some morphometric traits. Correlation results indicated that in female goats, BW had a significantly positive correlation (P < 0.01) with HG (r = 0.84). While in male goats, BW had a highly significant positive correlation (P < 0.01) with BL (r = 0.79). The CART model indicated that HG was the best predictor variable for BW, while MARS found BL > 77 cm as the best predictor of BW in the study. CART outperformed MARS model with high R 2  = 0.76, Adj R 2  = 0.76 and r = 0.87. The study concludes that HG can be used as a predictor of BW.

Objective: The aim of the study was to use a meta-analysis to identify the correlation between linear body measurements, including body length (BL), wither height (WH), heart girth (HG), and body volume (BV), and body weight in beef cattle by breed, sex, and age as categories. Materials and Methods: These results can be used as a method for predicting beef cattle body weight. This study used systematic review and meta-analysis guidelines to create a checklist. The first stage was searching for papers relevant to the study objectives. The second stage was searching using the keywords beef cattle, body weight, body measurement, and correlation. The third stage was reviewing the title and abstract. The fourth stage was abstracting information from selected papers, and the last stage was tabulating data. Results: The results from this study were obtained, and 32 papers were eligible for the meta-analysis stage. The correlation between linear body measurement and body weight of beef cattle showed that HG (r = 0.88) and BV (r = 0.97) were significantly (p < 0.05) different compared to BL (r = 0.74) and WH (r = 0.72). The correlation between HG and body weight, and the categorization of cattle breeds showed significantly (p < 0.05) different results. The correlation between BV and body weight of cattle according to breed categories showed results that were not significantly (p > 0.05) different, while age was significantly (p < 0.05). Conclusion: In conclusion, to predict beef cattle body weight, it is necessary to use HG or BV, with breed, sex, and age of cattle as categories.

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking.

This study examined changes in body measurements among Korean adolescents aged 16–19 before and after the COVID-19 pandemic, using Size Korea data from 2015 and 2023. A total of 245 participants from each year (2015: n = 245; 2023: n = 245) were matched by age and sex to ensure comparability. Thirty-five anthropometric variables were analyzed using independent samples t-tests and two-way ANOVA to assess differences by year and sex. The results revealed significant structural changes characterized by an upper-body–centered shift in morphology, with notable increases in chest breadth and depth, waist and abdominal circumferences, and upper-arm girth. While height increased overall, arm and thigh lengths showed significant decreases, indicating restricted limb growth alongside torso-dominant development. Chi-square analyses further revealed significant post-pandemic shifts in BMI distributions, with a decrease in the proportion of normal-weight adolescents and an increase in overweight and obesity, particularly among males. Sex-specific differences were also evident: male adolescents experienced greater increases in torso breadths, depths, and body weight, whereas females showed marked gains in chest, waist, and hip circumferences with relatively stable BMI. These findings suggest that lifestyle disruptions during the pandemic, such as reduced physical activity and increased sedentary behavior, contributed not only to weight gain but also to altered body proportions that were not fully captured by BMI alone.

Body measurement plays a crucial role in cattle breeding selection. Traditional manual measurement of cattle body size is both time-consuming and labor-intensive. Current automatic body measurement methods require expensive equipment, involve complex operations, and impose high computational costs, which hinder efficient measurement and broad application. To overcome these limitations, this study proposes an efficient automatic method for cattle body measurement. Lateral and dorsal image datasets were constructed by capturing cattle keypoints characterized by symmetry and relatively fixed positions. A lightweight SCW-YOLO keypoint detection model was designed to identify keypoints in both lateral and dorsal cattle images. Building on the detected keypoints, 11 body measurements—including body height, chest depth, abdominal depth, chest width, abdominal width, sacral height, croup length, diagonal body length, cannon circumference, chest girth, and abdominal girth—were computed automatically using established formulas. Experiments were performed on lateral and dorsal datasets from 61 cattle. The results demonstrated that the proposed method achieved an average relative error of 4.7%. Compared with the original model, the parameter count decreased by 58.2%, compute cost dropped by 68.8%, and model size was reduced by 57%, thus significantly improving lightweight efficiency while preserving acceptable accuracy.

The aim of this study was to identify breeding practices and trait preferences for indigenous sheep in three districts (Estie, Farta and Lay Gayient) located in the northwest highlands of Ethiopia. Questionnaire survey and choice experiment methods were used to collect data from 370 smallholder farmers. Respondents were selected randomly among smallholder farmers who own sheep in the aforementioned districts. A generalized multinomial logit model was employed to examine preferences for sheep attributes, while descriptive statistics and index values were computed to describe sheep breeding practices. Having the highest index value of 0.36, income generation was ranked as the primary reason for keeping sheep, followed by meat and manure sources. The average flock size per smallholder farmer was 10.21 sheep. The majority of the smallholder farmers (91%) have the experience of selecting breeding rams and ewes within their own flock using diverse criteria. Given the highest index value of 0.34, body size was ranked as a primary ram and ewe selection criteria, followed by coat color. Furthermore, choice modeling results revealed that tail type, body size, coat color, growth rate, horn and ear size have shown significant influences on smallholder farmers' preference for breeding rams (P<0.01). The part-worth utility coefficients were positive for all ram attributes except ear size. For breeding ewes, mothering ability, coat color, body size, lambing interval, growth rate, tail type and litter size have shown significant effects on choice preferences of smallholder farmers (P<0.05). Moreover, significant scale heterogeneity was observed among respondents for ewe attributes (P<0.001). Overall, the results implied that sheep breeding objectives suitable for the northwest highlands of the country can be derived from traits such as linear body measurement, weight and survival at different ages, and lambing intervals. However, selection decisions at the smallholder level should not only be based on estimated breeding values of traits included in the breeding objective but instead, incorporate ways to address farmers' preference for qualitative traits.

The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live body weight of Savanna goats remains poorly understood. The study was conducted to investigate the influence of linear body measurements on the body weight of Savanna goats using MARS. In total, 173 Savanna goats between the ages of two and five years were used to collect body weight (BW), body length (BL), heart girth (HG), rump height (RH) and withers height (WH). MARS was used as a data mining algorithm for data analysis. The best predictive model was achieved from the training dataset with the highest coefficient of determination and Pearson’s correlation coefficient (0.959 and 0.961), respectively. BW was influenced positively when WH > 63 cm and HG >100 cm with a coefficient of 0.51 and 2.71, respectively. The interaction of WH > 63 cm and BL < 75 cm, WH < 68 cm and HG < 100 cm with a coefficient of 0.28 and 0.02 had a positive influence on Savanna goat BW, while male goats had a negative influence (−4.57). The findings of the study suggest that MARS can be used to estimate the BW in Savanna goats. This finding will be helpful to farmers in the selection of breeding stock and precision in the day-to-day activities such as feeding, marketing and veterinary services.

Body weight (BW) at maturity is an essential trait in poultry farming as it determines the price of the chickens in most formal markets. However, the chicken growth rate is more critical as a performance-monitoring tool for farmers. Nonetheless, it is difficult to predict these two traits while chickens are still young without expensive means. Therefore, this study sought to identify traits from among the linear body measurements (LBM) that can be used to predict both the growth rate and final BW while chickens are still young. The study was conducted in Chongwe District of Zambia using one hundred and thirty-two (132) 8-weeks old Sasso T431 cockerels. The cockerels were managed using small-scale farmer free-range management system. The study found an overall growth rate of 18.10g/day and chickens reached a final BW of 2312.61 g at the end of the 12 weeks. Correlation analysis found that all LBM were positively correlated with the growth rate and final BW except the SC, which was negatively correlated, suggesting that selection for any LBM with a positive correlation in week eight would increase body weight and growth rate. Path analysis identified corpus length to have the highest direct influence on growth rate and BW. The Keel Length was identified to have the greatest indirect influence on growth rate and mature body weights. Therefore, farmers can use corpus length at 8 weeks to select chickens that will be expected to grow bigger and faster.