Weight Estimation Research Articles

From Avatars to Agents: Self-Related Cues Through Embodiment and Personalization Affect Body Perception in Virtual Reality.

Our work investigates the influence of self-related cues in the design of virtual humans on body perception in virtual reality. In a $2\times 2$ mixed design, 64 participants faced photorealistic virtual humans either as a motion-synchronized embodied avatar or as an autonomous moving agent, appearing subsequently with a personalized and generic texture. Our results unveil that self-related cues through embodiment and personalization yield an individual and complemented increase in participants' sense of embodiment and self-identification towards the virtual human. Different body weight modification and estimation tasks further showed an impact of both factors on participants' body weight perception. Additional analyses revealed that the participant's body mass index predicted body weight estimations in all conditions and that participants' self-esteem and body shape concerns correlated with different body weight perception results. Hence, we have demonstrated the occurrence of double standards through induced self-related cues in virtual human perception, especially through embodiment.

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO & Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

Weighted Centroid Amorphous Algorithm for Position Error Minimization in WSN

ABSTRACTWireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Validation of Fetal Medicine Foundation charts for fetal growth in twins: nationwide Danish cohort study.

To assess the validity of the Fetal Medicine Foundation (FMF) chorionicity-specific models for fetal growth in twin pregnancy. This was an external validation study of the FMF models using a nationwide Danish cohort of twin pregnancies. The cohort included all dichorionic (DC) and monochorionic diamniotic (MCDA) twin pregnancies with an estimated delivery date between 2008 and 2018, which satisfied the following inclusion criteria: two live fetuses at the first-trimester ultrasound scan (11-14 weeks' gestation); biometric measurements available for the calculation of estimated fetal weight (EFW) using the Hadlock-3 formula; and delivery of two liveborn infants. Validation involved assessing the distributional properties of the models and estimating the mean EFW Z-score deviations. Additionally, the models were applied to pregnancies that delivered preterm and attended non-scheduled visits (complicated pregnancies). Overall, 8542 DC and 1675 MCDA twin pregnancies met the inclusion criteria. In DC twins, 17 084 fetuses were evaluated at a total of 95 346 ultrasound scans, of which 44.5% were performed at scheduled visits in pregnancies carried to 37 + 0 weeks or later. The median number of growth scans per DC twin fetus from 20 + 0 weeks onwards was four. The model showed good agreement with the validation cohort for scheduled visits in DC twins delivered at 37 + 0 weeks or later (mean EFW Z-score, -0.14 ± 1.05). In MCDA twins, 3350 fetuses underwent 31 632 eligible ultrasound scans, of which 59.5% were performed at scheduled visits in pregnancies carried to 36 + 0 weeks or later. The median number of growth scans per MCDA twin fetus from 16 + 0 weeks onwards was 10. The model showed favorable agreement with the validation cohort for scheduled visits in MCDA twins delivered at 36 + 0 weeks or later (mean EFW Z-score, -0.09 ± 1.01). Non-scheduled visits and preterm delivery before 37 + 0 weeks for DC twins and before 36 + 0 weeks for MCDA twins corresponded with smaller weight estimates, which was consistent with the study's definition of complicated pregnancy. The FMF models provide a good fit for EFW measurements in our Danish national cohort of uncomplicated twin pregnancies assessed at routine scans. Therefore, the FMF models establish robust criteria for subsequent investigations and potential clinical applications. Future research should focus on exploring the consequences of clinical implementation, particularly regarding the identification of twins that are small-for-gestational age, as they are especially susceptible to adverse perinatal outcome. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Delineating the drought vulnerability zones in Bangladesh

The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.

Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.

Deep‐Learning‐Based Facial Retargeting Using Local Patches

AbstractIn the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch‐based retargeting method that transfers facial animations captured in a source performance video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source video frame. These patches are processed through the Reenactment Module to generate correspondingly re‐enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.

Vaginal delivery following induction and associated factors among laboring women at South Wollo Zone Public Hospitals of Ethiopia, 2023

Improving maternal and perinatal outcomes can be achieved by identifying factors affecting successful vaginal delivery following induction of labor, particularly in developing countries with low rates of induction. Besides this, evidence regarding the magnitude of successful vaginal delivery following induction and associated factors is limited in Ethiopia. Therefore, this study aimed to assess the magnitude of vaginal delivery following induction and associated factors among laboring women at South Wollo Zone Public Hospitals of Ethiopia, 2023. An institutional-based cross-sectional study was conducted among 385 from April 1 to June 15, 2023. A systematic random sampling method was used to select each participant. A pretested structured interviewer-administered questionnaire and checklist were used to collect data. Epi-Data version 4.6 and SPSS version 26 software were used for data entry and analysis, respectively. Multivariable binary logistic regression was used to identify associated factors and an adjusted odds ratio with a 95% confidence interval was used to identify significant variables. Model fitness was checked using the Hosmer-Lemeshow goodness of fit test. The study reported that 75.6% of participants gave vaginal delivery following induction with a 95% CI (71.00-79.80). Mid-upper arm circumstance 23–28 cm (AOR = 2.55, 95% CI: 1.19–5.47), multiparty (AOR = 3.01, 95% CI: 1.430–6.33), favorable bishop (AOR = 3.79, 95% CI: 1.74–8.26), oxytocin with cervical ripening method (AOR = 3.74, 95% CI: 1.99–7.04), and birth weight less than 4000gram (AOR = 5.40, 95% CI: 1.54–18.91) were factors significantly associated with successful vaginal delivery following induction. Therefore, obstetric caregivers should consider pre-induction assessments such as bishop score of the cervix and fetal weight estimation and improving the nutritional status of pregnant women.

A comment on manuscript Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.

A comment on manuscript Comparison of machine learning algorithms and multiple linear regression for live weight estimation of Akkaraman lambs.

Non-stationary mechanical sound source separation: An all-neural beamforming network driven by time–frequency convolution and self-attention

To tackle the challenging task of extracting target signal features amidst significant interference from complex, time-varying non-stationary noise in industrial scenarios, we propose a novel time-domain all-neural beamforming network tailored for non-stationary mechanical sound source separation, named TFANBNet. Leveraging time–frequency convolution and self-attention, TFANBNet promised robust performance in challenging acoustic environments. First, the proposed time–frequency convolution module employs parameterized complex-valued convolutional layers to simulate time–frequency transformations, thereby implicitly extracting time–frequency features. Then, the proposed adaptive attention transformer network enhances local attention interaction by integrating intermediate features, effectively enhancing long-range context modeling. Third, the beamforming module utilizes deep neural networks for beamforming weight estimation, replacing conventional noise covariance matrix computations, which have inherent performance limitations. Finally, the experimental results on the multi-channel spatial reverberation dataset synthesized from the MIMII dataset show that the proposed TFANBNet yields superior separation performance and generalization capabilities compared to the considered competitive methods.

Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

The Azawak zebu bred at the Toukounous experimental centre in Niger: reproduction performance and estimation of genetic parameters.

The aim of this study was to estimate the genetic parameters of the reproductive performance of the Azawak zebu, bred on station during the period 1981 to 2007, in order to contribute to the implementation of an effective selection programme for improving the zootechnical performance of cattle herds. The data contained records of 2453 calvings from 650 Azawak cows, daughters of 55 bulls, all bred at the Toukounous experimental centre (Niger). Non-genetic factors were evaluated using linear mixed models and genetic parameters were estimated using REML procedures with a multi-trait animal model. The effects of year and calving rank were significant (P < 0.001) for the three reproductive traits studied. Heritability was low for age at first calving (h2 = 0.09 ± 0.03) and calf birth weight (h2 = 0.07 ± 0.03). It was moderate for calving interval (h2 = 0.30 ± 0.04) indicating the possibility of genetic improvement of this trait by selection. Repeatability estimates for calf birth weight and calving interval were 0.10 ± 0.02 and 0.42 ± 0.03 respectively. Calving interval had very strong and significant genetic correlations with age at first calving (0.84 ± 0.08) and calf birth weight (-0.40 ± 0.20). There was also a significantly favourable genetic correlation between calf weight at birth and age at first calving (-0.32 ± 0.25), implying a simultaneous improvement in all three reproductive traits through selection. An increase in heritability of calf birth weight was observed after the active phase of the program, reflecting an overly open selection scheme. These genetic parameter estimates suggest that the three reproductive traits could be used in selection index with the aim to improve the three reproductive traits of Azawak cattle.

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Preliminary outcomes on phenotypic and genetic parameter estimates for body weight of indigenous Tswana goats in Botswana

The aims of this study were to estimate the genetic and phenotypic parameters for growth traits and evaluate genetic trends on 585 indigenous Tswana goats. The population was maintained under low input production system at the Department of Agricultural Research in Lesego ranch, Botswana, from 2005 to 2008. Data included birth weight (BW), weaning weight (WW), weight at 8 months (PW), yearling weight (YW), pre-weaning average daily gain (ADG) and two post weaning average daily gains (ADG2 and ADGYW). Data was analysed using general linear model of SAS to determine non-genetic effects. Estimation of genetic and phenotypic parameters were estimated using ASREML fitting an animal model that accounted for fixed effect of parity, sex, type of birth and year of birth. Least squares means for BW, WW, PW and YW were 2.88 ± 0.03, 12.15 ± 0.17, 16.52 ± 0.28 and 21.04 ± 0.32Kg, respectively, while those for ADG, ADG2 and ADGYW were 74.52 ± 1.41, 28.78 ± 1.55 and 33.66 ± 2.28 g/day, respectively. Estimates of heritability for BW, WW, PW and YW were 0.79 ± 0.11, 0.63 ± 0.14, 0.32 ± 0.13 and 0.48 ± 0.16, respectively. The genetic correlations for all the traits studied were positive and moderate to high (0.48 to 0.82) whilst phenotypic correlations ranged from 0.21 to 0.72. Positive average genetic trends of 12.32% (WW), 13.39% (PW) and 7.38% (YW) were attained. The results have demonstrated the potential of this breed to be improved through selection.

GeoHi-GNN: Geometry-aware hierarchical graph representation learning for normal estimation

Normal estimation has been one of the key tasks in point cloud analysis, while it is challenging when facing with severe noises or complex regions. The challenges mainly come from the selection of supporting points for estimation, that is, improper selections of points and points' scale will lead to insufficient information, loss of details, etc. To this end, this paper proposes one feature-centric fitting scheme, GeoHi-GNN, by learning geometry-aware hierarchical graph representation for fitting weights estimation. The main functional module is the continuously conducted Hierarchically Geometric-aware (HG) module, consisting of two core operations, namely, the graph node construction (GNC) and the geometric-aware dynamic graph convolution (GDGC). GNC aims to aggregate the feature information onto a smaller number of nodes, providing global-to-local information while avoiding the interferences from noises in larger scales. With these nodes distributed in different scales, GDGC dynamically updates the node features regarding to both intrinsic feature and extrinsic geometric information. Finally, the hierarchical graphical features are cascaded to estimate the weights for supporting points in the surface fitting. Through the extensive experiments and comprehensive comparisons with the state-of-the-arts, our scheme has exhibited many attractive advantages such as being geometry-aware and robust, empowering further applications like more accurate surface reconstruction.

Deriving disability weights for the Netherlands: findings from the Dutch disability weights measurement study

BackgroundThe aims of this study were to establish national disability weights based on the health state preferences of a Dutch general population sample, examine the relation between results and respondent’s characteristics, and compare disability weights with those estimated in the European disability weights study.MethodsIn this cross-sectional study, a web-based survey was administered to a general population 18–75 years from the Netherlands. The survey included paired comparison questions. Paired comparison data were analysed using probit regression and located results onto the 0-to-1 disability weight scale using non-parametric regression. Bootstrapping was used to estimate 95% uncertainty intervals (95%UI). Spearman’s correlation was used to investigate the relation of probit regression coefficients between respondent’s characteristics.Results3994 respondents completed the questionnaire. The disability weights ranged from 0.007 (95%UI: 0.003–0.012) for mild distance vision impairment to 0.741 (95% UI: 0.498–0.924) for intensive care unit admission. Spearman’s correlation of probit coefficients between sub-groups based on respondent’s characteristics were all above 0.95 (p < 0.001). Comparison of disability weights of 140 health states that were included in the Dutch and European disability weights study showed a high correlation (Spearman’s correlation: 0.942; p < 0.001); however, for 76 (54.3%) health states the point estimate of the Dutch disability weight fell outside of the 95%UI of the European disability weights.ConclusionsRespondent’s characteristics had no influence on health state valuations with the paired comparison. However, comparison of the Dutch disability weights to the European disability weights indicates that health state preferences of the general population of the Netherlands differ from those of other European countries.

Fuzzy Logic Based Adaptive Parameter Estimation System in Moving Measurement Systems

Fast and accurate weighing of the weighing systems used in industrial filling systems is of great importance in terms of increasing production capacity and maintaining product quality. In facilities that grind and process grain, machines and equipment are positioned horizontally and vertically on steel structures. Since these machines continuously perform grinding, transferring, filling, and emptying operations, they create continuous vibration in the mechanical systems they are connected to. Moving weighing systems are significantly affected by these mechanical systems. When the impact effect of pneumatic valves-controlled covers in moving weighing systems is added to these structural mechanical vibrations, there are significant waits and delays in weighing systems that measure performance. For this reason, in a performance measurement system in a flour mill, the measurement interval increases as the amount of weighing increases. For example, in a moving weighing system that performs 50 kg performance weighing, the measurement interval can increase up to 15 seconds, which is quite long. In this study, an applied study has been conducted to increase the weighing performance in moving weighing systems and to minimize the measurement interval. The data collection process in the study focuses on two main components: load cell data and IMU data. Thus, it is aimed to overcome the difficulties of traditional methods used in weighing systems, which are generally observed to be insufficient to combat slow and noisy data. The analysis techniques used in this study are Kalman Filtering, Dynamic Q and R Matrix Updates, Comparative Analysis and Statistical Analysis. The Kalman filter was used for the integration of Load cell and IMU data and was applied to filter out noise and oscillations in the weighing data and make more accurate weight estimates. The results obtained showed that the dynamic Kalman filtering method can provide faster and more accurate weighing results compared to traditional methods, with error rates varying between 0.4% and 1% for different combinations of Q and R values in measurements made on the scale. Dynamic Kalman filtering method effectively filters oscillatory and noisy load cell signals, with error rates of 0.7% to 1% for Q=0.02 and R=17 parameters, and error rates of 0.4% to 0.7% for Q=0.07 and R=13 parameters. was able to obtain more accurate weight estimates. This study has shown that the dynamic Kalman filtering method is a potential method that can be used in industrial filling systems. This method can contribute to increasing production capacity and maintaining product quality by providing faster and more accurate weighing results. In this respect, the research has a unique contribution. This method provides a revolutionary development in industrial weighing systems and fills an important gap in the literature.

A Comparative Study of Fetal Weight Estimation by Symphysiofundal Height and Ultrasound Biometry in Women at Term

A Comparative Study of Fetal Weight Estimation by Symphysiofundal Height and Ultrasound Biometry in Women at Term

Accurate birth weight prediction from fetal biometry using the Gompertz model

ObjectivesMonitoring of fetal growth and estimation of birth weight is of clinical importance. During pregnancy, ultrasound fetal biometry values including femur length, head circumference, abdominal circumference, biparietal diameter are measured and used to place fetuses on “growth charts”. There is no simple growth-model-based, predictive formula in use for fetal biometry. Estimation of fetal weight at birth currently depends on ultrasound data taken a short time before birth. Study designOur cohort (“Seethapathy cohort”) consists of ultrasound biometry measurements and other data for 774 pregnant women in Chennai, India, 2015–2017. We use the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, to model the growth of fetal biometry, and a machine learning (ML) model trained on these parameters to predict birth weight (BW). ResultsThe Gompertz model convincingly fits the growth of fetal biometry values. Two Gompertz parameters—t0 (inflection time) and c (rate of decrease of growth rate)—seem universal to all fetuses, while the third, A, is an overall scale specific to each fetus, capturing individual variation. On the Seethapathy cohort we can infer A for each fetus from ultrasound data available by the 24 or 35 weeks. Our ML model predicts birth weight with < 8 % error, outperforming published methods that have access to late-term ultrasound data. The same model gives an 8.4 % error in BW prediction on an independent validation cohort of 365 women. ConclusionsThe Gompertz model fits fetal biometry growth and enables birth weight estimation without need of late-term ultrasounds. Aside from its clinical predictive value, we suggest its use for future growth standards, with almost all variation described by a single scale parameter A.

Total body weight estimation by 3D camera systems: Potential high-tech solutions for emergency medicine applications? A scoping review.

Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High-tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods. A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated. A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care. The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.