Citation

Shan G, Bohn C. Appl. Ergon. 2003; 34(4): 327-337.

Affiliation

Department of Kinesiology, The University of Lethbridge, 4401 University Drive, Lethbridge, Canada AB T1 K 3M4. g.shan@uleth.ca

Copyright

(Copyright © 2003, Elsevier Publishing)

DOI

unavailable

PMID

15729773

Abstract

As a result of migration and globalization, the requirement for anthropometrical data of distinct races and gender has augmented whilst the availability remained minimal. Therefore, several sets of estimation equations, which depend on gender, race, body height (BH), and body mass (BM), were established in this study to fulfill this necessity. The method consisted of: (a) an inexpensive device to scan the body surface, (b) the electronic reconstruction of the body surface and (c) a module to calculate segmental lengths, segmental masses, radii of gyration and moments of inertia, using the 16-segment model (Zatsiorsky, 1983) and density data of Dempster (Space requirements of the seated operator, WADC Technical Report, Wright-Patterson Air Force Base, Ohio, 1995, pp. 55-159), and (d) the establishment of regression equations. One hundred young Chinese and Germans, representing the Asian and Caucasian races, were randomly recruited to participate in this study. The results revealed contrasting trunk, limb lengths and relative skull volume (skull volume/body volume) between the two races as well as the independence of head mass from body height. The regression equations, which were successfully derived based on the above-unveiled differences, are capable of supplying a prompt way to obtain all anthropometrical parameters of different genders and race groups through individual BM and BH. Anthropometrical data are related to gender, race, BH and BM. In order to obtain the data, one can utilize various measurements, which might have enormous financial expenditure in addition to time-consumption or employ the convenient and economical short-cut-regression-to obtain such data. The results of this study reveal that the accuracy of such estimations is high. The errors of predictions lie under 0.7 Standard deviation, which will satisfy most of applications.

Language: en