3D anthropometric investigation of head and face characteristics of Australian cyclists

Design specialists have acknowledged the need for more accurate measurements of human anthropometry through the use of 3D data, especially for the design of head and facial equipment. However, 3D anthropometric surveys of the human head are sparse in the literature and practically non-existent for Australia. Research published to date has not proposed concrete methods that can accurately address the hair thickness responsible for inaccurate representation of the head's shape. This study used a state-of-theart handheld white light scanner to digitize 3D anthropometric data of 222 participants in the Melbourne Metropolitan Area. The participants volunteered for the study consisted of 46 females and 176 males (age: 34.6 ± 12.5). The participants' head scans were aligned to a standard axis system, whereby a Hair Thickness Offset (HTO) method was introduced to more accurately describe the true shape of the head. It is envisaged that the database constructed through this research can be used as a reference for the design and testing of helmets in Australia

APPLICATION OF THE ARTEC EVA SCANNER FOR ORTHOTICS IN PRACTICE

Tento článok sa týka spôsobov, ktoré sú k dispozícii na skeneri Artec Eva v oblasti protetiky a protetiky so zameraním na tvár a krk. Metodika je rozdelená do 5 základných skupín - príprava prostredia, skener, predmety, skenovanie a 3D modely. Praktická časť obsahuje 3D vybrané vybrané objekty, ktoré obsahujú ukážky iných druhov, konkrétne oblasti dosahu vlasov, brady a oblasti očí

A parametric product design framework for the development of mass customized head/face (Eyewear) products

This study led to the development of a parametric design method for mass-customised head/face products. A systematic review of different approaches for mass customization was conducted, identifying advantages and limitations for their application to new product development. A parametric modelling algorithm of a 3D human face was developed using selected scanned 3D head models. The algorithm was developed from a set of measurable and adjustable parameter points related to the facial geometry. These parameters were defined using planimetry. Using the assigned parameter values as input, the parametric model generated 3D models of a human face that served as a reference for the design of customized eyewear. The current challenges and opportunities of mass customized head/face products are described, along with the possibilities for new parametric product design approaches to enable rapid manufacturing and mass customization. This study also explored whether a new parametric design framework for mass customization could be effectively implemented as an early-stage new product development strategy for head/face products

A 3D anthropometric sizing analysis system based on North American CAESAR 3D scan data for design of head wearable products

The present study developed a sizing analysis system for head-related product designs based on the Civilian American and European Surface Anthropometry Resource (CAESAR) database of North Americans. A total of 2299 heads in the CAESAR database were manually edited and 26 anthropometric landmarks were marked on the edited 3D heads to measure 30 anthropometric dimensions related to head-related product designs. The 3D anthropometric sizing analysis system (3D-ASAS) developed in the study provides analysis functions of a sizing system and representative face models by considering a target product, a target population, the number of size categories, and key anthropometric dimensions based on the CAESAR head measurements. Further research to reduce the efforts of manual editing and landmarking of 3D body scan data is discussed for efficient application of the 3D-ASAS to the design process of various wearable products.11Nsciescopu

Awareness of 3D Body-Scanning and Prospective Update of Indonesian Anthropometry for Virtual Fashion Design

It is a challenge for the designer and user in compromising a perfect fit of fashion. This study addresses the awareness of users and development of two different methods for the fitting of a fashion product. The first one, it is an individual anthropometric measurement, whereas the second one is assisted anthropometric measurement. Based on the Focused Group Discussion (FGD), it showed that the virtual fashion technology was perceived as something new and prospective in the future fashion industry. Through the virtual prototyping using CLO3D, it was found that the result had relatively the same as the manual measurement. The virtual one has reduced measurement and lead time significantly. In other words, the virtual measurement is deemed to be a time-saving process, promoting “fitting the product to the user” principle. This study supports a good communication bridge between users and designers through the 3D virtual clothing process, and also contributes to Indonesian anthropometry updates

Automatic parametric digital design of custom-fit bicycle helmets based on 3D anthropometry and novel clustering algorithm

Bicycle helmets can provide valuable protective effects to the wearer’s head in the event of a crash. However, the level of protection that helmets offer varies greatly between the users for similar impacts. Although these discrepancies can be due to many causes, several researchers highlighted the poor fit of helmets experienced by some users as a possible explanation. Poor helmet fit may be attributed to two main causes. First, the helmet could be worn incorrectly, with the helmet either worn back to front, or tilted forward or backward. The chin strap could also be unfastened. Second, helmet sizes and shapes available to the public might not be suitable for the full range of head morphologies observed in the population. Indeed, for some users, there could either be a large gap and/or pressure points between the inner surfaces of the helmet and the head, or a low coverage of the skull area with significant unprotected regions of the head. While the poorly informed usage of bicycle helmets is partly rectifiable through education programs, the mismatch between the head and the helmet’s inside surfaces primarily relates to the conventional design method and manufacturing techniques used in the industry today. In addition to the safety concerns described above, poorly fitted helmets can cause significant discomfort and may lead people to cycle infrequently or even not cycle altogether. Such a reaction could be somewhat detrimental to the user since the health benefits of regular cycling are significant. Some organisations and institutions even believe that the risks involved in cycling without a helmet (in not-extreme practices such as mountain biking) might be outweighed by the health benefits of consistent physical workout that the activity procures. However, this is impractical in countries such as Australia where mandatory helmet laws (MHL) are in place. Improper helmet fit coupled with MHL might be the reason why Australians cycle less than formerly, despite many initiatives undertaken by the government to grow the activity. In summary, current commercially available bicycle helmets suffer from the lack of fit accuracy, are uncomfortable, and consequently can discourage riding activities in the community, especially in populations like Australia where MHL exist. Therefore, the main purpose of this research has been to develop an innovative method to produce bicycle helmet models that provide a highly accurate fit to the wearer’s head. To achieve this goal, a mass customisation (MC) framework was initiated. MC systems enable the association of the small unit costs of mass production with the compliance of individual customisation. Although MC is defined as the use of both computer-aided design and manufacturing systems to produce custom output, it was decided to focus exclusively, in this study, on the design part of the MC framework of bicycle helmets. More specifically, I tried to answer the following central research question: How can one automatically create commercially ready, custom-fit digital 3D models of bicycle helmets based on 3D anthropometric data? One objective was to create certified design models, since helmets must comply with relevant safety regulations to be sold in a country. Safety standards generally determine the amount of energy a helmet must absorb during a crash, which mostly affects the thickness of its foam liner. Since customisation plays a major role in the helmet liner’s thickness, special considerations on how the automatic process should affect the helmet’s shape were provided. Contrary to conventional helmet production techniques, this method was based on state of the art technologies and techniques, such as three-dimensional (3D) anthropometry, supervised and unsupervised machine-learning methods, and fully parametric design models. Indeed, until today, traditional 1D anthropometric data (e.g., head circumference, head length, and head breath) have been the primary sources of information used by ergonomists for the design of user-centred products such as helmets. Although these data are simple to use and understand, they only provide univariate measures of key dimensions, and these tend to only partially represent the actual shape characteristics of the head. However, 3D anthropometric data can capture the full shape of a scanned surface, thereby providing meaningful information for the design of properly fitted headgear. However, the interpretation of these data can be complicated due to the abundance of information they contain (i.e., a 3D head scan can contain up to several million data points). In recent years, the use of 3D measurements for product design has become more appealing thanks to the advances in mesh parameterization, multivariate analyses, and clustering algorithms. Such analyses and algorithms have been adopted in this project. To the author’s knowledge, this is the first time that these methods have been applied to the design of helmets within a mass customisation framework. As a result, a novel method has been developed to automatically create a complete, certified custom-fit 3D model of a bicycle helmet based on the 3D head scan of a specific individual. Even though the manufacturing of the generated customised helmets is not discussed in detail in this research, it is envisaged that the models could be fabricated using either advanced subtractive and additive manufacturing technologies (e.g., numerical control machining and 3D printing.), standard moulding techniques, or a combination of both. The proposed design framework was demonstrated using a case study where customised helmet models were created for Australian cyclists. The computed models were evaluated and validated using objective (digital models) fit assessments. Thus, a significant improvement in terms of fit accuracy was observed compared to commercially available helmet models. More specifically, a set of new techniques and algorithms were developed, which successively: (i) clean, repair, and transform a digitized head scan to a registered state; (ii) compare it to the population of interest and categorize it into a predefined group; and (iii) modify the group’s generic helmet 3D model to precisely follow the head shape considered. To successfully implement the described steps, a 3D anthropometric database comprising 222 Australian cyclists was first established using a cutting edge handheld white light 3D scanner. Subsequently, a clustering algorithm, called 3D-HEAD-CLUSTERING, was introduced to categorize individuals with similar head shapes into groups. The algorithm successfully classified 95% of the sample into four groups. A new supervised learning method was then developed to classify new customers into one of the four computed groups. It was named the 3D-HEAD-CLASSIFIER. Generic 3D helmet models were then generated for each of the computed groups using the minimum, maximum, and mean shapes of all the participants classified inside a group. The generic models were designed specifically to comply with the relevant safety standard when accounting for all the possible head shape variations within a group. Furthermore, a novel quantitative method that investigates the fit accuracy of helmets was presented. The creation of the new method was deemed necessary, since the scarce computational methods available in the literature for fit assessment of user-centred products were inadequate for the complex shapes of today’s modern bicycle helmets. The HELMET-FIT-INDEX (HFI) was thus introduced, providing a fit score ranging on a scale from 0 (excessively poor fit) to 100 (perfect fit) for a specific helmet and a specific individual. In-depth analysis of three commercially available helmets and 125 participants demonstrated a consistent correlation between subjective assessment of helmet fit and the index. The HFI provided a detailed understanding of helmet efficiency regarding fit. For example, it was shown that females and Asians experience lower helmet fit accuracy than males and Caucasians, respectively. The index was used during the MC design process to validate the high fit accuracy of the generated customised helmet models. As far as the author is aware, HFI is the first method to successfully demonstrate an ability to evaluate users’ feelings regarding fit using computational analysis. The user-centred framework presented in this work for the customisation of bicycle helmet models is proved to be a valuable alternative to the current standard design processes. With the new approach presented in this research study, the fit accuracy of bicycle helmets is optimised, improving both the comfort and the safety characteristics of the headgear. Notwithstanding the fact that the method is easily adjustable to other helmet types (e.g., motorcycle, rock climbing, football, military, and construction), the author believes that the development of similar MC frameworks for user-centred products such as shoes, glasses and gloves could be adapted effortlessly. Future work should first emphasise the fabrication side of the proposed MC system and describe how customised helmet models can be accommodated in a global supply chain model. Other research projects could focus on adjusting the proposed customisation framework to other user-centred products

Resizable outerwear templates for virtual design and pattern flattening

The aim of this research was to implement a computer-aided 3D to 2D pattern development technique for outerwear. A preponderance of total clothing consumption is of garments in this category, which are designed to offer the wearer significant levels of ease. Yet there has not previously been on the market any system which offers a practical solution to the problems of 3D design and pattern flattening for clothing in this category. A set of 3D outerwear templates, one for men’s shirts and another for men’s trousers, has been developed to execute pattern flattening from virtual designs and this approach offers significant reduction in time and manpower involvement in the clothing development phase by combining creative and technical garment design processes into a single step. The outerwear templates developed and demonstrated in this research work can provide 3D design platforms for clothing designers to create virtual clothing as a surface layer which can be flattened to create a traditional pattern. Point-Cloud data captured by a modern white-light-based 3D body-scanning system were used as the basic input for creating the outerwear templates. A set of sectional curves, representative of anthropometric size parameters, was extracted from a virtual model generated from the body scan data by using reverse engineering software. These sectional curves were then modified to reproduce the required profile upon which to create items of men’s outerwear. The curves were made symmetrical, as required, before scaling to impart resizability. Using geometric modelling technique, a new surface was generated out of these resizable curves to form the required 3D outerwear templates. Through a set of functionality tests, it has been found that both of the templates developed in this research may be used for virtual design, 3D grading and pattern flattening

Development of statistical methodologies applied to anthropometric data oriented towards the ergonomic design of products

Ergonomics is the scientific discipline that studies the interactions between human beings and the elements of a system and presents multiple applications in areas such as clothing and footwear design or both working and household environments. In each of these sectors, knowing the anthropometric dimensions of the current target population is fundamental to ensure that products suit as well as possible most of the users who make up the population. Anthropometry refers to the study of the measurements and dimensions of the human body and it is considered a very important branch of Ergonomics because its considerable influence on the ergonomic design of products. Human body measurements have usually been taken using rules, calipers or measuring tapes. These procedures are simple and cheap to carry out. However, they have one major drawback: the body measurements obtained and consequently, the human shape information, is imprecise and inaccurate. Furthermore, they always require interaction with real subjects, which increases the measure time and data collecting. The development of new three-dimensional (3D) scanning techniques has represented a huge step forward in the way of obtaining anthropometric data. This technology allows 3D images of human shape to be captured and at the same time, generates highly detailed and reproducible anthropometric measurements. The great potential of these new scanning systems for the digitalization of human body has contributed to promoting new anthropometric studies in several countries, such as United Kingdom, Australia, Germany, France or USA, in order to acquire accurate anthropometric data of their current population. In this context, in 2006 the Spanish Ministry of Health commissioned a 3D anthropometric survey of the Spanish female population, following the agreement signed by the Ministry itself with the Spanish associations and companies of manufacturing, distribution, fashion design and knitted sectors. A sample of 10415 Spanish females from 12 to 70 years old, randomly selected from the official Postcode Address File, was measured. The two main objectives of this study, which was conducted by the Biomechanics Institute of Valencia, were the following: on the one hand, to characterize the shape and body dimensions of the current Spanish women population to develop a standard sizing system that could be used by all clothing designers. On the other hand, to promote a healthy image of beauty through the representation of suited mannequins. In order to tackle both objectives, Statistics plays an essential role. Thus, the statistical methodologies presented in this PhD work have been applied to the database obtained from the Spanish anthropometric study. Clothing sizing systems classify the population into homogeneous groups (size groups) based on some key anthropometric dimensions. All members of the same group are similar in body shape and size, so they can wear the same garment. In addition, members of different groups are very different with respect to their body dimensions. An efficient and optimal sizing system aims at accommodating as large a percentage of the population as possible, in the optimum number of size groups that better describes the shape variability of the population. Besides, the garment fit for the accommodated individuals must be as good as possible. A very valuable reference related to sizing systems is the book Sizing in clothing: Developing effective sizing systems for ready-to-wear clothing, by Susan Ashdown. Each clothing size is defined from a person whose body measurements are located toward the central value for each of the dimensions considered in the analysis. The central person, which is considered as the size representative (the size prototype), becomes the basic pattern from which the clothing line in the same size is designed. Clustering is the statistical tool that divides a set of individuals in groups (clusters), in such a way that subjects of the same cluster are more similar to each other than to those in other groups. In addition, clustering defines each group by means of a representative individual. Therefore, it arises in a natural way the idea of using clustering to try to define an efficient sizing system. Specifically, four of the methodologies presented in this PhD thesis aimed at segmenting the population into optimal sizes, use different clustering methods. The first one, called trimowa, has been published in Expert Systems with Applications. It is based on using an especially defined distance to examine differences between women regarding their body measurements. The second and third ones (called biclustAnthropom and TDDclust, respectively) will soon be submitted in the same paper. BiclustAnthropom adapts to the field of Anthropometry a clustering method addressed in the specific case of gene expression data. Moreover, TDDclust uses the concept of statistical depth for grouping according to the most central (deep) observation in each size. As mentioned, current sizing systems are based on using an appropriate set of anthropometric dimensions, so clustering is carried out in the Euclidean space. In the three previous proposals, we have always worked in this way. Instead, in the fourth and last approach, called kmeansProcrustes, a clustering procedure is proposed for grouping taking into account the women shape, which is represented by a set of anatomical markers (landmarks). For this purpose, the statistical shape analysis will be fundamental. This contribution has been submitted for publication. A sizing system is intended to cover the so-called standard population, discarding the individuals with extreme sizes (both large and small). In mathematical language, these individuals can be considered outliers. An outlier is an observation point that is distant from other observations. In our case, a person with extreme anthopometric measurements would be considered as a statistical outlier. Clothing companies usually design garments for the standard sizes so that their market share is optimal. Nevertheless, with their foreign expansion, a lot of brands are spreading their collection and they already have a special sizes section. In last years, Internet shopping has been an alternative for consumers with extreme sizes looking for clothes that follow trends. The custom-made fabrication is other possibility with the advantage of making garments according to the customers' preferences. The four aforementioned methodologies (trimowa, biclustAnthropom, TDDclust and kmeansProcrustes) have been adapted to only accommodate the standard population. Once a particular garment has been designed, the assessing and analysis of fit is performed using one or more fit models. The fit model represents the body dimensions selected by each company to define the proportional relationships needed to achieve the fit the company has determined. The definition of an efficient sizing system relies heavily on the accuracy and representativeness of the fit models regarding the population to which it is addressed. In this PhD work, a statistical approach is proposed to identify representative fit models. It is based on another clustering method originally developed for grouping gene expression data. This method, called hipamAnthropom, has been published in Decision Support Systems. From well-defined fit models and prototypes, representative and accurate mannequins of the population can be made. Unlike clothing design, where representative cases correspond with central individuals, in the design of working and household environments, the variability of human shape is described by extreme individuals, which are those that have the largest or smallest values (or extreme combinations) in the dimensions involved in the study. This is often referred to as the accommodation problem. A very interesting reference in this area is the book entitled Guidelines for Using Anthropometric Data in Product Design, published by The Human Factors and Ergonomics Society. The idea behind this way of proceeding is that if a product fits extreme observations, it will also fit the others (less extreme). To that end, in this PhD thesis we propose two methodological contributions based on the statistical archetypal analysis. An archetype in Statistics is an extreme individual that is obtained as a convex combination of other subjects of the sample. The first of these methodologies has been published in Computers and Industrial Engineering, whereas the second one has been submitted for publication. The outline of this PhD report is as follows: Chapter 1 reviews the state of the art of Ergonomics and Anthropometry and introduces the anthropometric survey of the Spanish female population. Chapter 2 presents the trimowa, biclustAnthropom and hipamAnthropom methodologies. In Chapter 3 the kmeansProcrustes proposal is detailed. The TDDclust methodology is explained in Chapter 4. Chapter 5 presents the two methodologies related to the archetypal analysis. Since all these contributions have been programmed in the statistical software R, Chapter 6 presents the Anthropometry R package, that brings together all the algorithms associated with each approach. In this way, from Chapter 2 to Chapter 6 all the methodologies and results included in this PhD thesis are presented. At last, Chapter 7 provides the most important conclusions

Evaluating garment size and fit for petit women using 3D body scanned anthropometric data

Research suggests that there is a plethora of information on the size and shape of the average and plus sized women in South Africa (Winks, 1990; Pandarum, 2009; Muthambi, 2012; Afolayan & Mastamet-Mason, 2013 and Makhanya, 2015). However, there is very little information on petite women‟s body shapes, their body measurements and their shopping behaviour, especially in South Africa, for manufacturing ready-to-wear garments. The purpose of this petite women study was to investigate the shapes and sizes of a sample of petite South African women and develop size charts for the upper and lower body dimensions. This study used a mixed-method; purposive, non-probability sampling method to achieve the objectives of the study. A (TC)² NX16 3D full body scanner and an Adam‟s® medical scale were used to collect the body measurement data of 200 petite South African women, aged between 20-54 years with an average height range of 157cm, residing in Gauteng (Pretoria and Johannesburg). Other data collection instruments included a demographic questionnaire to collect the subjects‟ demographic information such as, age, height, weight, etc.; and the psychographic questionnaire to gather the petite subjects‟ demographics as well as their perceptions and preferences on currently available ready-to-wear shirt and trouser garments. Of the 200 subjects that were initially recruited, based on the petite women‟s body height that ranged from 5‟ 4” (163 cm) and below, the most prevalent body shape profile that emerged from the dataset, was the pear body shape which was evident in 180 of the 3D full body scanned petite women subjects. Therefore, the anthropometric data for these 180 subjects was used in the development of the experimental upper and lower body dimensions size charts and as the basis for the fit test garments developed in this study. The collected data was analysed and interpreted in Microsoft Excel and the IBM SPSS Statistics 24 (2016) software package, using principal component analysis (PCA) to produce the experimental size charts for the upper and lower body dimensions necessary for creating prototype shirt and trouser garments. Regression analysis was used to establish the primary and secondary body dimensions for the development of the size charts and for determining the size ranges. The experimental upper and lower body dimensions size charts were developed for sizes ranging from size 6/30 to size 26/50. Subsequently, the accuracy of the size charts developed in this study was evaluated by a panel of experts who analysed the fit of the prototype shirt and trouser garments, manufactured using measurements for a size 10/34 size range from the size chart, on a sample of the petite subjects. The fit of these garments was also compared with the fit of garments manufactured using the 3D full body scanned measurements of a size 10/34 petite tailoring mannequin, that is currently commercially available for use in the production of garments for petite women in South Africa. The shirt and trouser prototype garments developed using the size 10/34 upper and lower body dimensions size chart measurements had, overall, a better quality of fit than the garments made to fit the current, commercially available, size 10/34 mannequin. These findings thereby confirmed that the data extracted from the (TC)² NX16 3D full body scanner and the size charts subsequently developed using the data, has the potential to provide better/improved fit in garments for petite South African women than data hitherto published. From the evidence of this study, it is recommended that the South African garment manufacturing industry needs to revise the current sizing system for petite women to accommodate the body dimensions and shape variations that currently prevail amongst consumers. The South African garment manufacturers and retailers also need to familiarise themselves with the needs, challenges and preferences of the petite consumers‟ target market that purchase ready-to-wear shirt and trouser garments in South Africa.Life and Consumer SciencesM.ConSci. (Department of Life and Consumer Science