Multi-resolution shape description and clustering of three-dimensional head data

3D human ear modelling with parameterization technique and variation analysis

Anthropometry is vital to provide design references when seeking proper product fit. Nowadays, 3D anthropometry is widely used to provide more size and shape details for improving product designs. However, 3D ear anthropometry is still at an explorative stage, considering the complex ear morphology and other technical obstacles. The proposed research method in this study is applicable to analyse the 3D point cloud of the entire external ear. With the cross-parameterisation technique, the dataset was used to explore the morphological characteristics of the ear. Ear dimensions were automatically extracted and further analysed to explore the gender and symmetry differences using two-way ANOVA. The 3D ear models were investigated through Principal Component Analysis (PCA). The most significant variation was found in the helix and concha region, and the overall ear size is the second important factor determining ear variance. The statistical models were generated as 3D design references for ear-related products.Practitioner summary: This study revealed the morphological variations of the entire 3D external ear with a parameterised 3D ear dataset. Based on the PCA findings, a set of statistical models were generated as design references for product evaluation digitally or physically.

A parametric design framework for the mass customization of bicycle helmet

Cluster analysis of 3D head shapes plays a crucial role in the mass customization design of products related to the head. Head shapes exhibit variations across different races, and designing helmets exclusively for Chinese individuals cannot solely rely on or reference foreign head models. Currently, research on cluster analysis of Chinese head shapes is limited, especially concerning shape variances. To address this, we developed an improved k-medoids algorithm and integrated Cluster Validity Index as an assessment metric. This enabled us to cluster 339 Chinese young males aged 18 to 30 into 7 groups based on their head shapes. By comparing our improved algorithm to the traditional k-medoids method, we affirmed its superiority in achieving higher sample participation rates and reducing inter-cluster sample disparities. To simplify the helmet design and editing process, and to improve the efficiency of mass customization, we have developed a parametric modeling program for bicycle helmets based on the head shape clustering results. Results from the Helmet Fit Index and stress simulation analysis demonstrate that our approach significantly enhances helmet fit and wearer comfort.

Fit, stability and comfort assessment of custom-fitted bicycle helmet inner liner designs, based on 3D anthropometric data

Research has demonstrated that a better-fitted bicycle helmet offers improved protection to the rider during an impact. Nowadays, bicycle helmets in the market that range in size from small/medium to medium/large might not fit the diverse range of human head shapes and dimensions. 3D scanning was used to create 3D head shape databases of 20 participants who volunteered for the study. We developed new custom-fitted helmet inner liners, based on the 3D head shape of two sub-groups of participants, to map their head sizes and contours closely to the conventional Medium (M) and Large (L) sizes as described in from AS/NZS 2512.1: 2009. The new custom-fitted helmet was compared with the helmet available in the market place in a dynamics stability test and from participants' subjective feedback. A significant reduction in the angle of helmet rotation on the headform in the lateral direction was recorded for the custom-fitted helmet. A Wilcoxon signed-rank test was conducted to evaluate participants' feedback on the helmets according to different area definitions. The overall fit and comfort and the top region of the new helmet were significantly improved. However, no difference was found at the significant level of 0.05 for the front and rear region of the new helmet.

Estimation of Human Body Volume (BV) from Anthropometric Measurements Based on Three-Dimensional (3D) Scan Technique

BACKGROUND

This study aimed to develop estimation formulae for the total human body volume (BV) of adult males using anthropometric measurements based on a three-dimensional (3D) scanning technique. Noninvasive and reliable methods to predict the total BV from anthropometric measurements based on a 3D scan technique were addressed in detail.

METHODS

A regression analysis of BV based on four key measurements was conducted for approximately 160 adult male subjects. Eight total models of human BV show that the predicted results fitted by the regression models were highly correlated with the actual BV (p < 0.001).

RESULTS

Two metrics, the mean value of the absolute difference between the actual and predicted BV (V _error) and the mean value of the ratio between V _error and actual BV (RV _error), were calculated. The linear model based on human weight was recommended as the most optimal due to its simplicity and high efficiency.

CONCLUSIONS

The proposed estimation formulae are valuable for estimating total body volume in circumstances in which traditional underwater weighing or air displacement plethysmography is not applicable or accessible.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Using subject-specific three-dimensional (3D) anthropometry data in digital human modelling: case study in hand motion simulation

Digital human modelling enables ergonomists and designers to consider ergonomic concerns and design alternatives in a timely and cost-efficient manner in the early stages of design. However, the reliability of the simulation could be limited due to the percentile-based approach used in constructing the digital human model. To enhance the accuracy of the size and shape of the models, we proposed a framework to generate digital human models using three-dimensional (3D) anthropometric data. The 3D scan data from specific subjects' hands were segmented based on the estimated centres of rotation. The segments were then driven in forward kinematics to perform several functional postures. The constructed hand models were then verified, thereby validating the feasibility of the framework. The proposed framework helps generate accurate subject-specific digital human models, which can be utilised to guide product design and workspace arrangement. Practitioner Summary: Subject-specific digital human models can be constructed under the proposed framework based on three-dimensional (3D) anthropometry. This approach enables more reliable digital human simulation to guide product design and workspace arrangement.

Digital 3-D headforms with facial features representative of the current US workforce

Existing headforms are based on anthropometric data collected over 30 years ago. In 2003, the National Institute for Occupational Safety and Health conducted an anthropometric survey of 3997 respirator users, of which 1013 subjects were scanned with a Cyberware 3-D Rapid Digitizer. The objective of this study was to create headforms representative of the current US workforce. Ten facial dimensions relevant to respirator fit were chosen for defining a principal component analysis model, which divides the user population into five face-size categories. Mean facial dimensions were then computed to target the ideal facial dimensions for each size category. Five scans in each category were chosen and averaged to construct a representative headform for each size category. Five digital 3-D headforms were developed: small, medium, large, long/narrow and short/wide. All dimensions are within 3 mm of the computed means for the sample population in each size category. STATEMENT OF RELEVANCE: This manuscript describes a new approach to constructing headforms that takes into account the facial form (size and shape) of the US workforce. These headforms could be incorporated into respirator research, certification standards and design in efforts to reduce the risk of injury or illness caused by inhalation hazards.

Resolution Influence on 3D Anthropometric Data Clustering for Fitting Design

Sizing based on 3D anthropometric data may lead to significant improvement in fitting comfort of wearing products. However, the required computational load is a common problem in 3D data processing. In a previous study, wavelet analysis was adopted to establish a multi-resolution description of 3D anthropometric data to reduce computational load and modeling complexity. K-means clustering was subsequently performed on the decomposed 3D samples. This study further examines the influence of decomposition level on clustering results. As a case study, 378 face samples, 447 head samples and 432 upper head samples were analyzed. Cluster membership variation on five different resolution levels was examined by using Cluster Membership Accuracy Rate (CMAR), which denotes the clustering consistency on the decomposed levels compared with the clustering results on the original data sets. For the face data sets, the CMAR values on the five decomposition levels are 100, 99.21, 97.88, 93.92 and 93.39%, respectively; for upper heads, the CMAR values are 99.3, 99.1, 98.4, 92.1 and 84.3%, respectively; while for whole heads, the CMAR values are 99.3, 98.2, 95.1, 85.5 and 77.9%, respectively. These results indicate that clustering on the third decomposition level is proper for face and head scans in reducing computational load while maintaining at least 95% clustering accuracy.