Three-Dimensional Body Scan Data Analysis

2008 ◽  
Vol 26 (3) ◽  
pp. 227-252 ◽  
Author(s):  
Adriana Petrova ◽  
Susan P. Ashdown
2019 ◽  
Vol 31 (6) ◽  
pp. 802-812
Author(s):  
Yeong Hoon Kang ◽  
Sungmin Kim

Purpose The purpose of this paper is to develop a system to design a bulletproof pad for chest protection using three-dimensional body scan data. Design/methodology/approach Body data were divided into arbitrary number of groups based on the standard normal distribution theory, considering the width and height of the upper body. Several parameters were used to define the cover area of the bulletproof pad, and the shape of this area of each model in a group was averaged to generate the standard bulletproof pad model for that group. Findings It is possible to use three-dimensional body scan data in the design process of a mass-customized bulletproof pad for chest protection. Practical implications It is expected that it would be possible to design not only bulletproof pad but also many kinds of body-related products that need to reflect the shape of body using the methodology developed in this study. Social implications Using this system, the mass customization of special garments and equipment would be possible, which will improve the wearers’ comfort and work efficiency. Originality/value Three-dimensional body measurement, parametric definition of cover area and user interface for shape modification developed in this study will facilitate the consumer-oriented product design.


2003 ◽  
Author(s):  
Melinda M. Cerney ◽  
Dean C. Adams ◽  
Judy M. Vance

2019 ◽  
Vol 32 (3) ◽  
pp. 446-456
Author(s):  
Yeonghoon Kang ◽  
Sungmin Kim

Purpose The purpose of this paper is to develop a software can generate helmet mold from three-dimensional (3D) human body scan data. Design/methodology/approach An algorithm has been developed to divide data into arbitrary number of groups considering the width, length and height of head using the standard normal distribution theory. A basic helmet mold is generated automatically based on the shape of representative convex hull for each group. Findings It is possible to analyze the 3D human body scan data of groups with various characteristics and apply them to mass customized production of helmet. Practical implications This methodology can be applied for designing other products related to the head shape such as goggles and masks by varying the measurement items of the head. Social implications This methodology will enable mass customized production centered on consumers in the production and design of various equipment and goods to be worn on the head. Originality/value An algorithm has been developed to define the vertex point, which is the limit of scan data, for the analysis of 3D human body scan data scan data. In addition, a system was developed that can mass-produce customized products by effectively dividing groups while taking into account the physical characteristics of consumers.


2015 ◽  
Vol 27 (3) ◽  
pp. 434-446 ◽  
Author(s):  
Sungmin Kim

Purpose – The purpose of this paper is to analyze automation of body surface shape. Design/methodology/approach – Numerous body landmarks are detected automatically. Body surface can be subdivided into multiple patches in a consistent manner using parametric design method. Findings – Complex surface shape of various human bodies can be analyzed easily and consistently. Research limitations/implications – The proposed method may not be applicable for a body with the shape which significantly differs from that of an average body. Practical implications – This method can greatly reduce the time required to analyze the surface shape of a three dimensional body scan data. Originality/value – The analysis of body surface shape is one of the most important processes especially in designing close fitting garments. The parametric design of body surface patches will facilitate the analysis of numerous body scan data.


2020 ◽  
Vol 1 (2) ◽  
pp. 75-82
Author(s):  
Salmun K. Nasib ◽  
Abas Kaluku ◽  
Abdul Wahab Abdullah

This article discusses the use of PowerPoint animation in learning with the aim of knowing the differences in learning outcomes of students whose learning uses power points and without using power points in three-dimensional topics. The method used is an experimental design with a True Experimental Design, namely Posttest-Only Design. The sampling technique used cluster random sampling. Student learning outcomes data were obtained through the learning outcome test instrument in the form of essays. Data analysis using descriptive analysis techniques and inferential data analysis. Hypothesis testing using a parametric analysis t-test. The results of the analysis show that the average learning outcomes of students who are taught using power points are higher than those of students taught conventionally. One of the factors that support the improvement of student learning outcomes is a learning approach to geometric shapes that involves interesting visualization. Interesting visualization makes students not just imagine something abstract but can directly observe the object being studied.


Author(s):  
Elizabeth Anne Shotton

Purpose The harbours of Ireland, under threat from deterioration and rising sea levels, are being documented using terrestrial LiDAR augmented by archival research to develop comprehensive histories and timeline models for public dissemination. While methods to extract legible three-dimensional models from scan data have been developed and such operational formats for heritage management are imperative, the need for this format in interpretive visualisations should be reconsidered. The paper aims to discuss these issues. Design/methodology/approach Interpretive visualisations are forms of history making, where factual evidence is drawn together with conjecture to illustrate a plausible account of events, and differentiation between fact and conjecture is the key to their intellectual transparency. A procedure for superimposing conjectural reconstructions, generated using Rhinoceros and CloudCompare, on original scan data in Cyclone and visualised on a web-based viewer is discussed. Findings Embellishing scan data with conjectural elements to visualise the evolution of harbours is advantageous for both research and public dissemination. The accuracy and density of the scans enables the interrogation of the harbour form and the irregular details, the latter in danger of generalisation if translated into parametric or mesh format. Equally, the ethereal quality of the point cloud conveys a sense of tentativeness, consistent with a provisional hypothesis. Finally, coding conjectural elements allows users to intuit the difference between fact and historical narrative. Originality/value While various web-based point clouds viewers are used to disseminate research, the novelty here is the potential to develop didactic representations using point clouds that successfully capture a provisional thesis regarding each harbour’s evolution in an intellectually transparent manner to enable further inquiry.


2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).


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