Helmy Mustafa

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 wearers head in the event of a crash, not every user benefits to the same extent when wearing the headgear. A proper fit with the cyclists 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 wearers 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.

A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head

ABSTRACTIn recent years, the use of 3D anthropometry for product design has become more appealing because of advances in mesh parameterisation, multivariate analyses and clustering algorithms. The purpose of this study was to introduce a new method for the clustering of 3D head scans. A novel hierarchical algorithm was developed, in which a squared Euclidean metric was used to assess the head shape similarity of participants. A linkage criterion based on the centroid distance was implemented, while clusters were created one after another in an enhanced manner. As a result, 95.0% of the studied sample was classified inside one of the four computed clusters. Compared to conventional hierarchical techniques, our method could classify a higher ratio of individuals into a smaller number of clusters, while still satisfying the same variation requirements within each cluster. The proposed method can provide meaningful information about the head shape variation within a population, and should encourage ergonomist...