感觉
材料科学
纹理(宇宙学)
感知
表面粗糙度
表面光洁度
复合材料
心理学
人工智能
计算机科学
图像(数学)
神经科学
作者
Margherita Raccuglia,Kolby Pistak,Christian Heyde,Jianguo Qu,Ningtao Mao,Simon Hodder,George Havenith
标识
DOI:10.1177/0040517517716905
摘要
This experiment studied textile (surface texture, thickness) and non-textile (local skin temperature changes, stickiness sensation and fabric-to-skin pressure) parameters affecting skin wetness perception under dynamic interactions. Changes in fabric texture sensation between WET and DRY states and their effect on pleasantness were also studied. The surface texture of eight fabric samples, selected for their different structures, was determined from surface roughness measurements using the Kawabata Evaluation System. Sixteen participants assessed fabric wetness perception, at high pressure and low pressure conditions, stickiness, texture and pleasantness sensation on the ventral forearm. Differences in wetness perception (p < 0.05) were not determined by texture properties and/or texture sensation. Stickiness sensation and local skin temperature drop were determined as predictors of wetness perception (r 2 = 0.89), and although thickness did not correlate with wetness perception directly, when combined with stickiness sensation it provided a similar predictive power (r 2 = 0.86). Greater (p < 0.05) wetness perception responses at high pressure were observed compared with low pressure. Texture sensation affected pleasantness in DRY (r 2 = 0.89) and WET (r 2 = 0.93). In WET, pleasantness was significantly reduced (p < 0.05) compared to DRY, likely due to the concomitant increase in texture sensation (p < 0.05). In summary, under dynamic conditions, changes in stickiness sensation and wetness perception could not be attributed to fabric texture properties (i.e. surface roughness) measured by the Kawabata Evaluation System. In dynamic conditions thickness or skin temperature drop can predict fabric wetness perception only when including stickiness sensation data.
科研通智能强力驱动
Strongly Powered by AbleSci AI