Anthropometry and Scan: A Computational Exploration on Measuring and Imaging
- 232 Downloads
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.
KeywordsAnthropometry 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).
- 3.Madden, A.M., Smith, S.: Body composition and morphological assessment of nutritional status in adults: a review of anthropometric variables. J. Hum. Nutr. Dietietics 29, 1–19 (2014)Google Scholar
- 6.Mölbert, S.C., et al.: Assessing body image in anorexia nervosa using biometric self-avatars in virtual reality: attitudinal components rather than visual body size estimation are distorted. Psycholol. Med. 73, 38–46 (2018)Google Scholar
- 11.Pleuss, J.D., et al.: A machine learning approach relating 3D body scans to body composition in humans. Eur. J. Clin. Nutr. (2018)Google Scholar
- 21.Ulijaszek, T.J. Lourie, J.A.: Intra- and inter-observer error in anthropometric measurement. Anthropometry, pp. 30–55Google Scholar
- 28.Weinberg, S.M., Naidoo, S., Govier, D.P., Martin, R.A., Kane, A.A., Marazita, M.L.: Anthropometric precision and accuracy of digital three-dimensional photogrammetry: comparing the Genex and 3dMD imaging systems with one another and with direct anthropometry. J. Craniofac. Surg. 17(3), 477–483 (2006)CrossRefGoogle Scholar
- 34.Sforza, C., de Menezes, M., Ferrario, V.: Soft- and hard-tissue facial anthropometry in three dimensions: what’s new. J. Anthropol. Sci. 91, 159–184 (2013)Google Scholar
- 35.Farkas, L.G., Eiben, O.G., Sivkov, S., Tompson, B., Katic, M.J., Forrest, C.R.: Anthropometric measurements of the facial framework in adulthood: age-related changes in eight age categories in 600 healthy white North Americans of European ancestry from 16 to 90 years of age. J. Craniofac. Surg. 15(2), 288–298 (2004)CrossRefGoogle Scholar
- 37.Robinette, K.M., Daanen, H., Paquet, E.: The CAESAR project: a 3-D surface anthropometry survey. In: Second International Conference on 3-D Digital Imaging and Modeling (Cat. No. PR00062), pp. 380–386. IEEE (1999)Google Scholar