Anthropometry and Scan: A Computational Exploration on Measuring and Imaging

  • Michelle TotiEmail author
  • Cosimo Tuena
  • Michelle Semonella
  • Elisa Pedroli
  • Giuseppe Riva
  • Pietro Cipresso
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 288)


New developments in the field of technology have led to the use of scanners in order to obtain anthropometric measurements. As a matter of fact, anthropometry finds its roots in the seventeenth century, currently its usage has been strengthened by the employment of scanners. 3D whole-body scanners allow to collect reliable data and to visualise the exact human body shape. Thus, this paper aims at exploring the combination of these topics, anthropometry and scan, through an innovative tool, the scientometrics analysis. This technique provides a clear overview of the existing literature in the field investigated. In our study we examined 1’652 papers from the Web of Science Core Collection database. Network analyses have shown an interesting scenario, emphasising the research evolution over time. Specifically, endocrinology and metabolism emerged as the most active publication domains. Accordingly, the two most high-impact journals and the most cited paper regard nutrition issues and metabolic risk factors respectively. However, the predominance of the USA for number of publications has not been confirmed by the institution’s analysis, which has shown the University of Copenhagen as the most influential one. On the other hand, Yumei Zhang currently appears as the main authority in the field and Leslie G. Farkas as the most influential author over the entire time span analysed. The relevant implications of the findings are discussed in terms of future research lines.


Anthropometry Scan 3D scan Anthropometric measurements Scientometrics analysis Network analysis 



The present work was supported by the European funded project “Body-Pass”-API-ecosystem for cross-sectional exchange of 3D personal data (H2020-779780).


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Michelle Toti
    • 1
    Email author
  • Cosimo Tuena
    • 1
  • Michelle Semonella
    • 1
  • Elisa Pedroli
    • 1
  • Giuseppe Riva
    • 1
    • 2
  • Pietro Cipresso
    • 1
    • 2
  1. 1.Applied Technology for Neuro-Psychology LabIRCCS Istituto Auxologico ItalianoMilanItaly
  2. 2.Department of PsychologyUniversità Cattolica del Sacro CuoreMilanItaly

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