Assessing the female figure identification technique's reliability as a body shape classification system

This paper demonstrates the effects of slight differences in measurement definitions on resultant body shape classification. Ergonomic researchers consider the Female Figure Identification Technique (FFIT) a 'gold standard' body shape classification system to describe variation in a popula...

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Veröffentlicht in:Ergonomics 2021-08, Vol.64 (8), p.1035-1051
Hauptverfasser: Parker, Christopher J., Hayes, Steven George, Brownbridge, Kathryn, Gill, Simeon
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Sprache:eng
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Zusammenfassung:This paper demonstrates the effects of slight differences in measurement definitions on resultant body shape classification. Ergonomic researchers consider the Female Figure Identification Technique (FFIT) a 'gold standard' body shape classification system to describe variation in a population's 3 D profile. Nevertheless, researchers use FFIT without a scientific basis or considering their ergonomic suitability. This paper rigorously evaluates FFIT, focussing on ergonomics, garment construction, and scientific research applications. Through analysing 1,679 3 D Body Scans, we assess the level of agreement between the FFIT's body shape classification when measurements placed following FFIT's or SizeUK's guidance. We establish how different interpretations of FFIT's measurement placement cause the same body to be categorised into different shapes - in up to 40% of cases. FFIT omits shoulder measurements that have little relationship to body shape yet are vital in garment construction. Using FFIT with different datasets and definitions, therefore, leads to inconsistent conclusions about shape differences. To increase the effectiveness of body shape classification, research must appraise current systems through statistics. This paper demonstrates how current body definitions are too unspecific and exclude relevant body morphology for garment construction. Our paper suggests alternative anthropometrics and demographics for inclusion in a more advanced model.
ISSN:0014-0139
1366-5847
DOI:10.1080/00140139.2021.1902572