Accurate fit is imperative in compressive wearables for movement-sensing, where functional performance can be negatively affected by factors such as garment migration and looseness. Iterative production of sensing wearables for formative testing is resource-demanding, however, and estimating compressive fit is complicated due to variable textile properties and nonlinear strain effects. This work presents (1) a novel method for quantitatively developing and evaluating sizing systems for compression wearables and (2) testing of variable input parameters in an example use case of compressive products. Anthropometric data (10 circumferential measurements) were extracted from 74 body scans to test: four garment designs covering different portions of the leg; properties of three textiles; and four size strategies (5, 7, 10, and 15 sizes). The percentage of individuals accommodated was measured for these simulated sizing systems. Results indicated both material properties and garment type strongly affected accommodation, while the number of sizes yielded relatively small effects. The garment design incorporating the fewest body measurements accommodated the most bodies and increases in accommodation (up to 41.9%) were observed when removing measurements at the thigh and above. The textile with a weaker initial linear stress–strain slope and nonlinear curve was originally theorized to provide more accommodation for all conditions; however, for some garment designs a material with more moderate mechanical properties was the most accommodating, which highlights the complexity of nonlinear tensile properties paired with material thickness effects. In addition, insights are extracted from findings to support efficient compressive garment prototyping.
Beaudette et al. (2026) studied this question.