Human wetness perception in relation to textile water absorption parameters under static skin contact
Source abstract
Abstract
Skin wetness perception (WP) greatly affects thermal and sensorial discomfort in clothing and as such is of great interest to the clothing industry. Following neurophysiological studies of WP, this study looks at textile parameters affecting WP. Twenty-four fabrics varying in thickness, fiber type and absorption capacity were studied. Using 12 participants (males/females), the WP induced was studied in four wetness states: 1. Dry; 2. absolute (ABS), all having the same absolute water content of 2400 µL per sample (=0.24 µL mm −2 ); 3. 100REL, saturated with water to their individual absorption capacity; 4. 50REL, to 50% of the value in 3. As total absorption capacity was highly correlated ( r = 0.99) to fabric thickness, conditions 3 and 4 were equivalent to having the same water content per volume of textile, i.e. 0.8 and 0.4 µL mm −3 , respectively. Samples were applied to the upper back statically to minimize the contribution of surface roughness/friction. WP was highly correlated to drop in skin temperature induced by the wet fabric, and increased with application pressure of the fabric. No effect of fiber type was observed. In REL, with equal µL mm −3 , WP showed a positive correlation to total fabric water-content-per-area (µL mm −2 ), and thus also to thickness, given the correlation between the latter two, with saturation above 1.5 µL mm −2 . In ABS, on the other hand, with equal µL mm −2 , and thus with relative water content (µL mm µL mm −3 ) inversely proportional to thickness, WP was also inversely proportional to thickness. Thus WP showed opposing responses depending on the wetting type, indicating that the methodology of manipulating water content should be selected in relation to the product end-use.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Color perception and design · Tactile and Sensory Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Environmental Ergonomics Research Centre, Loughborough University, UK
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