Designing a Human-Centric Rigid Body Armor for Female Police Officers: The Implications of Fit on Performance and Gender Inclusivity

The lack of availability of female plates for police officers is an issue that has not been analyzed. Female anthropometry is uniformly different from male anthropometry. Currently available hard plates are flat. These plates may decrease coverage while increasing feelings of poor fit, discomfort, and poor mobility for both male and female officers. The plates designed for males offer the possibility of female officers experiencing feelings of gender exclusion. This research project explored the current perceptions of male and female police officers in Arkansas across the dimensions of fit, comfort, and mobility in the context of hard plate body armor. Perceptions of gender exclusion were explored through a gender ostracism scale. A female police officer of the population was the subject of a case study which explored the influence of a prototype plate from a 3D body scan on perceptions of fit, comfort, mobility, coverage, and inclusion. Range of motion was evaluated through goniometer measurements and perceptions were evaluated through Likert-scale responses and open-ended response. The outcomes of the research project included a proposed methodology for the development and evaluation of 3D modelled prototype plates

Parametric design for human body modeling by wireframe-assisted deep learning

Statistical learning of human body shape can be used for reconstructing or estimating body shapes from incomplete data, semantic parametric design, modifying images and videos, or simulation. A digital human body is normally represented in a high-dimensional space, and the number of vertices in a mesh is far larger than the number of human bodies in public available databases, which results in a model learned by Principle Component Analysis (PCA) can hardly reflect the true variety in human body shapes. While deep learning has been most successful on data with an underlying Euclidean or grid-like structure, the geometric nature of human body is non-Euclidean, it will be very challenging to perform deep learning techniques directly on such non-Euclidean domain. This paper presents a deep neural network (DNN) based hierarchical method for statistical learning of human body by using feature wireframe as one of the layers to separate the whole problem into smaller and more solvable sub-problems. The feature wireframe is a collection of feature curves which are semantically defined on the mesh of human body, and it is consistent to all human bodies. A set of patches can then be generated by clustering the whole mesh surface to separated ones that interpolate the feature wireframe. Since the surface is separated into patches, PCA only needs to be conducted on each patch but not on the whole surface. The spatial relationships between the semantic parameter, the wireframe and the patches are learned by DNN and linear regression respectively. An application of semantic parametric design is used to demonstrate the capability of the method, where the semantic parameters are linked to the feature wireframe instead of the mesh directly. Under this hierarchy, the feature wireframe acts like an agent between semantic parameters and the mesh, and also contains semantic meaning by itself. The proposed method of learning human body statistically with the help of feature wireframe is scalable and has a better quality

Apparel recommendation system evolution: an empirical review

Purpose: With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approach: This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords. Findings: This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors' research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/value: Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective

Feature-Based Human Model for Digital Apparel Design

Three-dimensional (3D) body scanning technology opens opportunities for virtual try-on and automatic made-to-measure apparel design. This paper proposes a new feature-based parametric method for modeling human body shape from scanned point clouds of a 3D body scanner $[{rm TC}]^{2}$. The human body model consists of two layers: the skeleton and the cross sections of each body part. First, a simple skeleton model from the body scanner $[{rm TC}]^{2}$ system has been improved by adding and adjusting the position of joints in order to better address some fit issues related to body shape changes such as spinal bending. Second, an automatic approach to extracting semantic features for cross sections has been developed based on the body hierarchy. For each cross section, it is described by a set of key points which can be fit with a closed cardinal spline. According to the point distribution in point clouds, an extraction method of key points on cross sections has been studied and developed. Third, this paper presents an interpolation approach to fitting the key points on a cross section to a cardinal spline, in which different tension parameters are tested and optimized to represent simple deformations of body shape. Finally, a connection approach of body parts is proposed by sharing a boundary curve. The proposed method has been tested with the developed virtual human model (VHM) system which is robust and easier to use. The model can also be imported in a CAD environment for other applications