Skip to main content
SpringerLink
Log in

Anthropometry and Scan: A Computational Exploration on Measuring and Imaging

  • Conference paper
  • First Online:
Pervasive Computing Paradigms for Mental Health (MindCare 2019)

  • 601 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Utkualp, N., Ercan, I.: Anthropometric measurements usage in medical sciences. Biomed. Res. Int. 2015, 7 (2015)

    Article  Google Scholar 

  2. Breno, M., Leirs, H., Van Dongen, S.: Traditional and geometric morphometrics for studying skull morphology during growth in Mastomys natalensis (Rodentia: Muridae). J. Mammal. 92(6), 1395–1406 (2011)

    Article  Google Scholar 

  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 

  4. Heymsfield, S.B., et al.: Digital anthropometry: a critical review. Eur. J. Clin. Nutr. 72, 680–687 (2018)

    Article  Google Scholar 

  5. Pedroli, E., et al.: The use of 3D body scanner in medicine and psychology: a narrative review. In: Cipresso, P., Serino, S., Ostrovsky, Y., Baker, Justin T. (eds.) MindCare 2018. LNICST, vol. 253, pp. 74–83. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01093-5_10

    Chapter  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 

  7. Cornelissen, K.K., et al.: Body size estimation in women with anorexia nervosa and healthy controls using 3D avatars. Sci. Rep. 17, 15773 (2017)

    Article  Google Scholar 

  8. Wells, J.C.K., Ruto, A., Treleaven, P.: Whole-body three-dimensional photonic scanning: a new technique for obesity research and clinical practice. Int. J. Obes. 32, 232–238 (2008)

    Article  Google Scholar 

  9. Ng, B.K., et al.: Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur. J. Clin. Nutr. 70, 1265 (2016)

    Article  Google Scholar 

  10. Haleem, A., Javaid, M.: 3D scanning applications in medical field: a literature-based review. Clin. Epidemiol. Glob. Health 7, 199–210 (2018)

    Article  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 

  12. Chen, C.: CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006)

    Article  Google Scholar 

  13. Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  Google Scholar 

  14. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35 (1977)

    Article  Google Scholar 

  15. Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7(4), 373–397 (2003)

    Article  MathSciNet  Google Scholar 

  16. González-Teruel, A., González-Alcaide, G., Barrios, M., Abad-García, M.F.: Mapping recent information behavior research: an analysis of co-authorship and co-citation networks. Scientometrics 103(2), 687–705 (2015)

    Article  Google Scholar 

  17. Orosz, K., Farkas, L.J., Pollner, P.: Quantifying the changing role of past publications. Scientometrics 108(2), 829–853 (2016)

    Article  Google Scholar 

  18. Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24(4), 265–269 (1973)

    Article  MathSciNet  Google Scholar 

  19. Bu, Y., Liu, T.Y., Huang, W.B.: MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations. Scientometrics 108(1), 143–166 (2016)

    Article  Google Scholar 

  20. White, H.D., Griffith, B.C.: Author cocitation: a literature measure of intellectual structure. J. Am. Soc. Inf. Sci. 32(3), 163–171 (1981)

    Article  Google Scholar 

  21. Ulijaszek, T.J. Lourie, J.A.: Intra- and inter-observer error in anthropometric measurement. Anthropometry, pp. 30–55

    Google Scholar 

  22. Madden, A.M., Smith, S.: Body composition and morphological assessment of nutritional status in adults: a review of anthropometric variables. J. Hum. Nutr. Diet. 29(1), 7–25 (2016)

    Article  Google Scholar 

  23. Cole, T.J., Bellizzi, M.C., Flegal, K.M., Dietz, W.H.: Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320(7244), 1240–1243 (2000)

    Article  Google Scholar 

  24. Yusuf, S., et al.: Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control study. Lancet 366(9497), 1640–1649 (2005)

    Article  Google Scholar 

  25. Kahn, H.S., Austin, H., Williamson, D.F., Arensberg, D.: Simple anthropometric indices associated with ischemic heart disease. J. Clin. Epidemiol. 49(9), 1017–1024 (1996)

    Article  Google Scholar 

  26. Treleaven, P., Wells, J.: 3D Body scanning and healthcare applications. Computer 40(7), 28–34 (2007)

    Article  Google Scholar 

  27. Tzou, C.-H.J., et al.: Comparison of three-dimensional surface-imaging systems. J. Plast. Reconstr. Aesthet. Surg. 67(4), 489–497 (2014)

    Article  Google 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)

    Article  Google Scholar 

  29. Haarbo, J., Gotfredsen, A., Hassager, C., Christiansen, C.: Validation of body composition by dual energy X-ray absorptiometry (DEXA). Clin. Physiol. 11(4), 331–341 (1991)

    Article  Google Scholar 

  30. Jensen, S.M., Mølgaard, C., Ejlerskov, K.T., Christensen, L.B., Michaelsen, K.F., Briend, A.: Validity of anthropometric measurements to assess body composition, including muscle mass, in 3-year-old children from the SKOT cohort. Matern. Child Nutr. 11(3), 398–408 (2015)

    Article  Google Scholar 

  31. Yu, K., Xue, Y., He, T., Guan, L., Zhao, A., Zhang, Y.: Association of spicy food consumption frequency with serum lipid profiles in older people in China. J. Nutr. Health Aging 22(3), 311–320 (2018)

    Article  Google Scholar 

  32. Zhao, A., et al.: Knowledge, attitude, and practice (KAP) of dairy products in Chinese urban population and the effects on dairy intake quality. Nutrients 9(7), 668 (2017)

    Article  Google Scholar 

  33. Ferrario, V.F., Sforza, C., Zanotti, G., Tartaglia, G.M.: Maximal bite forces in healthy young adults as predicted by surface electromyography. J. Dent. 32(6), 451–457 (2004)

    Article  Google 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)

    Article  Google Scholar 

  36. Farkas, L.G., Katic, M.J., Forrest, C.R.: International anthropometric study of facial morphology in various ethnic groups/races. J. Craniofacial Surg. 16(4), 615–646 (2005)

    Article  Google 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 

  38. Fox, C.S., et al.: Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116(1), 39–48 (2007)

    Article  Google Scholar 

  39. Ball, R., Shu, C., Xi, P., Rioux, M., Luximon, Y., Molenbroek, J.: A comparison between Chinese and Caucasian head shapes. Appl. Ergon. 41(6), 832–839 (2010)

    Article  Google Scholar 

  40. Daanen, H.A.M., Ter Haar, F.B.: 3D whole body scanners revisited. Displays 34(4), 270–275 (2013)

    Article  Google Scholar 

  41. Daanen, H.M., van de Water, G.J.: Whole body scanners. Displays 19(3), 111–120 (1998)

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20145, Milan, Italy

    Michelle Toti, Cosimo Tuena, Michelle Semonella, Elisa Pedroli, Giuseppe Riva & Pietro Cipresso

  2. Department of Psychology, Università Cattolica del Sacro Cuore, 20100, Milan, Italy

    Giuseppe Riva & Pietro Cipresso

Authors
  1. Michelle Toti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cosimo Tuena
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michelle Semonella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elisa Pedroli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Riva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pietro Cipresso
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michelle Toti .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Auxologico Italiano, Milan, Italy

    Pietro Cipresso

  2. Department of Clinical Neurosciences, University of Lausanne, Lausanne, Switzerland

    Silvia Serino

  3. Catholic University of the Sacred Heart, Milan, Italy

    Daniela Villani

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Toti, M., Tuena, C., Semonella, M., Pedroli, E., Riva, G., Cipresso, P. (2019). Anthropometry and Scan: A Computational Exploration on Measuring and Imaging. In: Cipresso, P., Serino, S., Villani, D. (eds) Pervasive Computing Paradigms for Mental Health. MindCare 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-25872-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-25872-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25871-9

  • Online ISBN: 978-3-030-25872-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation