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

The helmet fit index - A method for the computational analysis of fit between human head shapes and bicycle helmets

While a bicycle helmet protects the wearer's head in the event of a crash, not every user benefits to the same extent when wearing the headgear. A proper fit with the cyclist's head is found to be one of the most important attributes to improve protection during impact. A correct fit is defined as a small and uniform distance between the helmet liner and the wearer's head shape, with a broad coverage of the head area. The scientific community has recognised the need for improved fitting, but in-depth methods to analyse and compare the fit performance of distinct helmets models are still absent from the literature. We present a method based on 3D anthropometry, reverse engineering techniques and computational analysis to redress this shortcoming. As a result of this study, we introduce the Helmet Fit Index (HFI) as a tool for fit analysis between a helmet model and a human head. It is envisaged that the HFI can provide detailed understanding of helmet efficiency regarding fit and should be used during helmet development phases and testing

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

The drop impact test and dynamic stability test of the custom-fit user centred bicycle helmet using Finite Element Analysis

The current sizing of a bicycle helmet is available to cater for the general head sizes, ranging from S/M and L/XL, but there is also a universal model that can fit all sizes through adjustable helmet straps. However, based on the reported human anthropometric data, the human head shape and dimensions are different according to ethnic group, age and gender [1-3]. Furthermore, numerous surveys addressed the discomfort in wearing a helmet, and the current sizing did not accommodate the range users [4-7]. Asian users also reported they were experiencing poor fit when wearing a helmet because most helmets are designed according to the size of Western heads [2, 8]. Therefore, it can be concluded that the general size of helmets currently available in the market could not accommodate the range of human head shapes and dimensions. One possible solution to overcome the helmet “fit” problem for each user is the customized “user-centred” or “subject-specific” helmet design approach. The key to facilitating this approach to bicycle helmet is to build the inner liner according to the contour and shape of the head of each person. However, it is also important to note that changing the liner thickness and shape to improve helmet fit might influence the safety aspects of the bicycle helmet, such as the helmet liner impact attenuation properties and helmet dynamic stability. Since the user-centred design approach is quite new and has not been adopted previously in the bicycle helmet design, there is a lack of information on this area in the literature. This has motivated the author to bridge the knowledge gap, and therefore the primary aim of this research is to investigate the safety performance of a user-centred helmet liner design in drop impact test and dynamic stability test. The tests were performed using validated finite element (FE) models specifically developed for each test. In the end, a new framework was developed to test and validate the mass customised system of a new automated user-centred bicycle helmet design. Apart from its primary function as a protective item, impact strength is one of the most important aspects to be considered when designing a bicycle helmet [9]. The author has performed experimental drop impact tests on three commercial helmet models to gather important information to develop an FE model of the drop impact test. The author has also used new correlation methods, specifically created for the helmet impact test, to validate the simulation model according to the experimental results. The correlation methods are the Peak Score (PS), the Impact Duration Score (IDS) and the statistical Pearson correlation score. Very good correlation scores (more than 80%, in the scale of 0%-100%) between experimental and simulation results have been achieved using the aforementioned methods, and this indicates that the simulation model is consistent, accurate and reliable. Another important criterion for the bicycle helmet is the dynamic stability performance. The degree of helmet rotation, usually called the roll-off angle is observed, and the helmet will fail the test if the helmet completely comes of the head form. From the literature review, it was found that a very limited FE model has been previously developed to simulate dynamic stability test of a bicycle helmet. To fill the knowledge gap, a dynamic stability FE model was developed using rotational velocity as the input load to the helmet assembly. Again, the author has performed experimental dynamic stability tests on commercial bicycle helmets using a test rig specifically constructed for that purpose. The FE helmet model was observed to move and roll on the headform, similar to the helmet movement and behaviour recorded in the experiment. The Roll-off Score (RoS) results also showed that the FE model achieved comparably very similar results to those from the experiment. It should also be noted that a high-accuracy 3D (45μm accuracy) scanner was used to capture an accurate 3D representation of bicycle helmet components for both FE models. Another high-accuracy portable scanner (resolution up to 0.5mm, accuracy up to 0.1mm) was also used to scan the head shapes of participants in this study to create the customized user-centred bicycle helmet. The author also used the developed FE models to compare the performance of the user-centred bicycle helmet with the current helmet model in the drop impact tests and dynamic stability tests. Geomagic Studio 12 software was used to create the user-centred bicycle helmet based on the original commercial bicycle helmet design, where the inside part of the helmet was modified to follow the scanned head shape and size of participants, while the outside part of the helmet remained unchanged. This comparison has not been published in the literature before, and therefore it is a significant new knowledge. The result revealed that the user-centred bicycle helmet design influences the peak linear acceleration (PLA) of a helmet in an impact test. Due to the different head shape of each participant, it was observed that PLA increased when liner thickness is reduced at certain test area and decreased when liner thickness is increased. This information is important when designing the framework of customization of user-centred bicycle helmet design to make sure each user-centred helmet would pass the test without testing each of this custom helmet every time. It was also revealed that the rate of increase of the PLA is different according to the impact location when different liner thicknesses of the same helmet model were tested and compared. Moreover, foam density also influences the PLA, and higher PLA was noticed when the foam is either too hard (high-density) or too soft (low-density). A ranking of design factor influences on drop impact performance has also been established. The helmet liner thickness was found to have the most influence on impact properties of a bicycle helmet, followed by the impact location and liner density. In a dynamic stability test, the user-centred helmet was found to have a lower roll-off angle and hence performed better than the original helmet, when tested using the customised headform, made according to the head shape of each participant. This significant result strongly suggests that helmet fit improves the dynamic stability of bicycle helmet. It was also revealed that helmet dynamic stability performance was not strongly influenced by the helmet liner density because only a small difference in roll-off angles wasobserved for each helmet with different density. Conversely, dynamic stability was heavily influenced by the thickness of the liner. A helmet with thicker liner recorded a higher roll-off angle compared to one with a thinner liner. The fit of a user-centred helmet based on the commercial helmet model was compared to the original model with the standard sizing using Helmet Fit Index (HFI), using the standoff distance between the helmet and the head, as well as the helmet protection proportion. As expected, they have higher HFI than the original helmet with the standard size, indicating that the user-centred helmet has a better fit with the participant head shape compared to the helmet with the standard sizes. A new automated and customised bicycle helmet design has been developed within the research group. Using this tool, a customised bicycle helmet is developed using the digital data of head scan of an individual. For certification and testing purpose, the system created four headform groups based on the 122 participants of a cyclist community in Australia. A novel approach to creating the Maximum Head Shape (MaH) and Minimum Head Shape (MiH) of each group was proposed to test the new helmet design in a drop impact test and dynamic stability test. The worst-case helmet is created based on the Maximum Head Shape (MaH), while the best-case helmet is created using the Minimum Head Shape (MiH) of the group. This method was adopted in a case study of only a group, and we could ensure that each customised helmet design in that group would pass the drop impact test and dynamic stability test. The methods of using best-case and worst-case helmets as limitation eliminate the necessity to test each customised helmet created based on the head shape of the participant