A quantile-based anthropometry synthesis technique for global user populations

Detailed anthropometric data are valuable in making well-informed and responsible design decisions. However, such data are available only for a few user populations around the world. More widely-available information is in the form of summary statistics (e.g., means and standard deviations) and the values of body measures at certain key percentiles (e.g., 5^th, 50^th, 95^th). Such information, while useful, is not suitable for in-depth analyses of a population's variability, since it does not allow for the consideration of correlations between different body measures, does not describe irregular distributions of body dimensions, etc. This paper presents a new method that utilizes values of body measures at different percentiles in synthesizing a detailed anthropometric database for a virtual population of users. The procedure is demonstrated in the context of Japanese civilian youth and U.S. military, and is shown to be simple, accurate, easy to use, and applicable across these two anthropometrically dissimilar populations. The case study shows that the virtual population is statistically equivalent to the actual target population in a number of ways. In addition to achieving statistical equivalence with the actual population's body dimensions, the method also ensures that the synthesized individuals are composed of appropriate and realistic body proportions and combinations of anthropometry.     

Considering just noticeable difference in assessments of physical accommodation for product design

Configuring products or environments for the size of their human users requires the consideration of several characteristics of the target user population, including body dimensions (anthropometry) and preferred interaction. Users are both adaptable and imperfect observers, which often makes it difficult for them to distinguish between candidate designs. This insensitivity is described by a concept called ‘just noticeable difference’, or JND. This paper presents an implementation of JND modelling and demonstrates how its use in the sizing of products or environments for target user populations can improve expected performance. Two facets of this problem are explored: (1) how experimental measures of JND for dimensional optimisation tasks may be obtained, and (2) how measures of JND may be included in models of user–device interaction for both adjustable and discretely sized products and the assumptions required. A case study demonstrating the collection and modelling of JND for a simple univariate problem is also presented.

Practitioner Summary: Since people are adaptable and imperfect observers, there exists a ‘just noticeable difference’ that can be considered when designing products and environments. When JND is modelled for a target population, less variability in design dimensions due to physical user requirements may be necessary. This paper considers JND in quantitative simulations of population accommodation.     

Limiting disproportionate disaccommodation in design for human variability

In the design of artefacts, tasks and environments for human use, the body dimensions of the target population are a critical element in spatial optimisation of the design. This study examines how the choices designers make affect the ability of different user groups to safely and effectively interact with a designed artefact. Due to the variability in body size and shape across different demographic groups, heterogeneous user populations are unlikely to experience uniform levels of performance. The associated variability in the rate of unacceptable user conditions is referred to here as disproportionate disaccommodation. This is both an ethical and a performance concern that can partially be addressed through improved design practice. Three methods for incorporating the consideration of user demographics and the corresponding variability in body size and shape are presented. They are compared with a baseline strategy in terms of accommodation and cost.     

Model-based early and rapid estimation of COSMIC functional size – An experimental evaluation

ContextFunctional size measurement methods are widely used but have two major shortcomings: they require a complete and detailed knowledge of user requirements, and they involve relatively expensive and lengthy processes.ObjectiveUML is routinely used in the software industry to effectively describe software requirements in an incremental way, so UML models grow in detail and completeness through the requirements analysis phase. Here, we aim at defining the characteristics of increasingly more refined UML requirements models that support increasingly more sophisticated – hence presumably more accurate – size estimation processes.MethodWe consider the COSMIC method and three alternative processes (two of which are proposed in this paper) to estimate COSMIC size measures that can be applied to UML diagrams at progressive stages of the requirements definition phase. Then, we check the accuracy of the estimates by comparing the results obtained on a set of projects to the functional size values obtained with the standard COSMIC method.ResultsOur analysis shows that it is possible to write increasingly more detailed and complete UML models of user requirements that provide the data required by COSMIC size estimation methods, which in turn yield increasingly more accurate size measure estimates of the modeled software. Initial estimates are based on simple models and are obtained quickly and with little effort. The estimates increase their accuracy as models grow in completeness and detail, i.e., as the requirements definition phase progresses.ConclusionDevelopers that use UML for requirements modeling can obtain an early estimation of the application size at the beginning of the development process, when only a very simple UML model has been built for the application, and can obtain increasingly more accurate size estimates while the knowledge of the product increases and UML models are refined accordingly.     

Parametric body shape model of standing children aged 3–11 years

A statistical body shape model (SBSM) for children was developed for generating a child body shape with desired anthropometric parameters. A standardised template mesh was fit to whole-body laser scan data from 137 children aged 3–11 years. The mesh coordinates along with a set of surface landmarks and 27 manually measured anthropometric variables were analysed using principal component (PC) analysis. PC scores were associated with anthropometric predictors such as stature, body mass index (BMI) and ratio of erect sitting height to stature (SHS) using a regression model. When the original scan data were compared with the predictions of the SBSM using each subject's stature, BMI and SHS, the mean absolute error was 10.4 ± 5.8 mm, and 95th percentile error was 24.0 ± 18.5 mm. The model, publicly available online, will have utility for a wide range of applications.

Practitioner Summary: A statistical body shape model for children helps to account for inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modelling approach is useful for reliable prediction of the body shape of a specific child with a few given predictors such as stature, body mass index and age.     

An updated estimate of the body dimensions of US children

Anthropometric data from children are important for product design and the promulgation of safety standards. The last major detailed study of child anthropometry in the USA was conducted more than 30 years ago. Subsequent demographic changes and the increased prevalence of overweight and obesity render those data increasingly obsolete. A new, large-scale anthropometric survey is needed. As an interim step, a new anthropometric synthesis technique was used to create a virtual population of modern children, each described by 84 anthropometric measures. A subset of these data was validated against limited modern data. Comparisons with data from the 1970s showed significant changes in measures of width and circumference of the torso, arms and legs. Measures of length and measurements of the head, face, hands and feet exhibited little change. The new virtual population provides guidance for a comprehensive child anthropometry survey and could improve safety and accommodation in product design.

Practitioner Summary: This research reviews the inadequacies of available sources of US child anthropometry as a result of the rise in the rates of overweight and obesity. A new synthesised database of detailed modern child anthropometry was created and validated. The results quantify changes in US child body dimensions since the 1970s.     

Body shape analyses of large persons in South Korea

Despite the prevalence of obesity and overweight, anthropometric characteristics of large individuals have not been extensively studied. This study investigated body shapes of large persons (Broca index ≥ 20, BMI ≥ 25 or WHR>1.0) using stature-normalised body dimensions data from the latest South Korean anthropometric survey. For each sex, a factor analysis was performed on the anthropometric data set to identify the key factors that explain the shape variability; and then, a cluster analysis was conducted on the factor scores data to determine a set of representative body types. The body types were labelled in terms of their distinct shape characteristics and their relative frequencies were computed for each of the four age groups considered: the 10s, 20s–30s, 40s–50s and 60s. The study findings may facilitate creating artefacts that anthropometrically accommodate large individuals, developing digital human models of large persons and designing future ergonomics studies on largeness.

Practitioner Summary: This study investigated body shapes of large persons using anthropometric data from South Korea. For each sex, multivariate statistical analyses were conducted to identify the key factors of the body shape variability and determine the representative body types. The study findings may facilitate designing artefacts that anthropometrically accommodate large persons.     

Toward an Understanding of Consumer Attitudes on Online Review Usage

ABSTRACT

With the rapid e-commerce growth and changes in consumers’ behaviors, many businesses are forced to adapt their business model to match their target customers’ needs. To provide consumers with more product details and increase their confidence in making online purchases, online businesses offer an online review as an alternative to physically interacting with a product. Although consumers have become familiar with the use of online product reviews, many aspects of user behavior toward the usage of online reviews are still not well understood. This study explores the factors underlying the acceptance of consumers’ online review usage when considering purchasing an item. The study results provide insight into the factors that affect customers’ use of online reviews prior to a purchase. This study furthers the body of knowledge that deals with online reviews and system usage, providing results that allow e-commerce businesses to adapt their business model to better fit consumers’ expectations.     

The kernel density estimate/point distribution model (KDE-PDM) for statistical shape modeling of automotive stampings and assemblies

The ability to effectively model dimensional variation of stampings and assemblies is an important tool for manufacturers to investigate, assess and control quality levels of their products. Statistical Process Control (SPC) and Six-Sigma approaches use the assumptions of statistical independence and normally distributed data to create quality process control guidelines which are predominantly used in industry. Multivariate statistical techniques such as Principal Components Analysis (PCA) have been more recently applied to automotive body assembly analysis in order to capture the surface co-linearity present in the dimensional variation of stampings and assemblies. This paper combines the Point Distribution Model, which is based on PCA, and Kernel Density Estimation to provide a statistical shape model (the KDE–PDM) that can deal with high dimensional data sets, represent correlated variation modes, and provide accurate estimates of the underlying shape distribution. Examples from FE simulation and production case studies are presented to highlight the advantages of the KDE–PDM over two other statistical shape models: the univariate shape model, and the original PDM. The KDE–PDM's capabilities make it particularly suited to variation monitoring and diagnosis of high dimensional measurement data sets made available by optical measurement devices, and some suggestions for its implementation are also presented.     

基于EM算法的多椭圆形目标检测

提出了一种椭圆形多目标检测算法。根据椭圆形目标在形状上与高斯分布相对应的特点,计算目标的特征均值和协方差矩阵,采用主元素法来描述椭圆形目标的大小、位置及主轴。对于包含多个目标的重叠区域,设计了一种加权的EM迭代算法,从而精确计算该区域的目标数、各椭圆的统计特性及位置、形状和大小。把该算法应用于水果及运动目标检测,获得稳定、精确的分割结果。     

结合区域生长与灰度重建的CT图像肺气管树分割

目的 CT图像中肺气管树分割对肺部疾病的精确定位与量化评估具有重要意义。针对不同气管分枝在大小、形状和密度分布方面的差异,提出一种区域生长与形态学灰度重建相结合的3维肺气管树分割流程,重点解决气管提取过程中的局部泄漏问题。方法 首先,采用阈值分割和形态学闭运算提取肺实质以定义感兴趣区域;然后,通过改进迟滞阈值区域生长法分割较粗气管,结合局部体积突变指标抑制侧向泄漏;接着,利用3维形态学灰度重建算法分割较细气管,并采用形状约束连接元分析和管形描述子剔除伪气管区域;最后,将上述两步分割结果融合成完整气管树。结果 采用EXACT’09竞赛提供的公开数据,选取20例测试图像及手工标记结果作为参考,分别从分杈点、分支数量和分支数比率等方面进行量化评估。实验结果表明,本文方法能在较低泄漏前提下成功检测出参考标准中一半以上的分支、平均分支数比率达到55.5%。结论 与竞赛网站公布的其他方法相比,本文方法结果评价指标处于中上水平,但算法简单、计算复杂度低、易于实现,在泄漏检测方面有一定优势。     

Anthropometric estimates for British civilian adults

Anthropometric data concerning British civilian adults is incomplete with respect both to the samples of people investigated and the measurements taken. The purpose of the present paper is to review the currently available sources and to provide (by estimation) a data set which is sufficiently comprehensive and accurate for general application in workspace design.

The method of estimation employed required a knowledge of the mean and standard deviation of stature in the target population. Statistical parameters of other body dimensions were obtained by scaling these down according to ratios previously determined for other reference populations. A previous study had indicated the magnitude of the errors to be anticipated in this procedure.

Anthropometric tables are provided for the adult male and female populations of Great Britain (aged 16-64 years). Percentile values are given for fifty bodily dimensions.     

Review Article A review of multi-channel indices of class separability

Abstract

An increasingly common question arising in processing digital multi-channel data from spacecraft and aircraft scanner systems is “which channels contain the best information to separate the classes of interest to the user?”. This may be to identify the best single channel for separating classes in a black and white photographic or line printer output product, or the best three channels for a colour photographic presentation of the data, or the best ‘n’ channels to enter into a classification (being mindful of the ‘trade off’ between improved sub-class separability and increasing usage of computer space and time resources). The only valid way to approach separability using more than one channel is to consider it in multi-channel space utilizing the inter-channel relationship terms. Having defined the classes using some form of hierarchical ordering approach, such as that proposed by Anderson et al. (1972), the user may compile the statistical profiles of the classes of interest from the sensor multi-channel data. Based on these statistics a number of multi-channel separability indices may be derived. Each of these indices quantifies, on the basis of the user-defined multi-channel statistics, the degree of inter-class separability the user can expect as a function of subsets of channels drawn from the overall sensor channel set. This review considers some of the more common multi-channel indices of separability and presents the links between them. Their various properties, and some limitations, are also presented as is an operational approach to their use.     

Plus size and obese workers: anthropometry estimates to promote inclusive design

Abstract

A significant proportion of the adult population globally is overweight, obese or classed as ‘plus size’. This has led to variability in size and shape across the working population and exclusion in the workplace. A new dataset of the anthropometry of plus size people has been created. Length dimensions were similar to other data, but breadth, circumference, and depth measurements were substantially larger. The hip breadth and abdominal depth were important for predicting largeness in this population. These data help explain the high exclusion rates from design and the number of fit, reach, posture and clearance issues reported by participants with a high BMI: generally, the higher the BMI the greater prevalence of problems. It is hoped that a better understanding of the anthropometric characteristics of the plus size worker will inform the design of safe, productive work environments to promote inclusion for a wider range of people.

Practitioner Summary: A new anthropometry dataset of plus size people has been created. The higher the BMI the greater the problems with design in the workplace for fit, reach, posture and clearance. To ensure inclusion and reduce stigma it is important to understand more about the size and shape of this population.     

On accurate and efficient statistical counting in sensor-based surveillance systems

Sensor networks have been used in many surveillance systems, providing statistical information about monitored areas. Accurate counting information (e.g., the distribution of the total number of targets) is often important for decision making. As a complementary solution to double-counting in communication, this paper presents the first work that deals with double-counting in sensing for wireless sensor networks. The probability mass function (pmf) of target counts is derived first. This, however, is shown to be computationally prohibitive when a network becomes large. A partitioning algorithm is then designed to significantly reduce computation complexity with a certain loss in counting accuracy. Finally, two methods are proposed to compensate for the loss. To evaluate the design, we compare the derived probability mass function with ground truth obtained through exhaustive enumeration in small-scale networks. In large-scale networks, where pmf ground truth is not available, we compare the expected count with true target counts. We demonstrate that accurate counting within 1%–3% relative error can be achieved with orders of magnitude reduction in computation, compared with an exhaustive enumeration-based approach.     

Application of bootstrap method in conservative estimation of reliability with limited samples

Accurate estimation of reliability of a system is a challenging task when only limited samples are available. This paper presents the use of the bootstrap method to safely estimate the reliability with the objective of obtaining a conservative but not overly conservative estimate. The performance of the bootstrap method is compared with alternative conservative estimation methods, based on biasing the distribution of system response. The relationship between accuracy and conservativeness of the estimates is explored for normal and lognormal distributions. In particular, detailed results are presented for the case when the goal has a 95% likelihood to be conservative. The bootstrap approach is found to be more accurate for this level of conservativeness. We explore the influence of sample size and target probability of failure on the quality of estimates, and show that for a given level of conservativeness, small sample sizes and low probabilities of failure can lead to a high likelihood of large overestimation. However, this likelihood can be reduced by increasing the sample size. Finally, the conservative approach is applied to the reliability-based optimization of a composite panel under thermal loading.     

An efficient human model customization method based on orthogonal-view monocular photos

Human body modeling is a central task in computer graphics. In this paper, we propose an intelligent model customization method, in which customer’s detailed geometric characteristics can be reconstructed using limited size features extracted from the customer’s orthogonal-view photos. To realize model customization, we first propose a comprehensive shape representation to describe the geometrical shape characteristics of a human body. The shape representation has a layered structure and corresponds to important feature curves that define clothing size. Next, we identify and model a novel relationship model between 2D size features and 3D shape features for each cross-section using real subject scanned data. We predict a customer’s cross-sectional 3D shape based on size features extracted from the customer’s photos, and then we reconstruct the customer’s shape representation using predicted cross-sections. We develop a new deformation algorithm that deforms a template model into a customized shape using the reconstructed 3D shape representation. A total of 30 subjects, male and female, with varied body shapes have been recruited to verify the model customization method. The customized models show high degree of resemblance of the subjects, with accurate body sizes; the accuracy of the models is comparable to scan. It shows that the method is a feasible and efficient solution for human model customization that fulfills the specific needs of the clothing industry.     

Comparison of statistical models performance in case of?segmentation using a small amount of training datasets

Model-based image segmentation has been extensively used in medical imaging to learn both the shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model, as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using MR images when only a small number of datasets is available for training. For shape, both PCA-based priors and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from EM clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.