An analysis of the variability of anatomical body references within ready-to-wear garment sizes

Citation data:

Proceedings of the 2016 ACM International Symposium on Wearable Computers - ISWC '16, Vol: 12-16-September-2016, Page: 84-91

Publication Year:
2016
Captures 10
Readers 10
Citations 1
Citation Indexes 1
DOI:
10.1145/2971763.2971800
Author(s):
Linsey Griffin, Crystal Compton, Lucy E. Dunne
Publisher(s):
Association for Computing Machinery (ACM)
Tags:
Computer Science
conference paper description
Establishing a range of sizes for apparel that can effectively fit the body shapes of a diverse population is a complex task, that for ready-to-wear (RTW) apparel is often reduced to a solution that is cost-feasible, if not optimal. While prototype garments are developed with specific fit objectives relative to an individual fit model, that shape is made larger and smaller according to a defined set of increments between sizes. For RTW, the objective in selecting size parameters is usually based on aesthetics. However, as garment-integrated technologies that require more precise placement of integrated technologies (such as sensors) on the body surface become more common in clothing, the implications of current RTW sizing techniques for precise on-body placement is not yet fully understood. Here, we present a comparison of the variability in anthropometrics of a target population and the variability assumed by a sizing standard, with respect to the impact of this disparity for placement of chest-mounted sensing devices like ECG electrodes. We analyze a large (n=3982) publically available anthropometric database and compare our findings with a smaller (n=140) sample of more specifically measured landmarks manually collected from 3D body scans. We find that RTW sizing results in problematic variability of landmark position for a large portion of the population, with potentially important implications for the placement of garment-integrated sensors. Results illustrate the need for consideration of nontraditional sizing strategies for garment-integrated sensing.

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