Neural network predictions of human psychological perceptions of clothing sensory comfort

The objective of this paper is to investigate the predictability of clothing sensory comfort from psychological perceptions by using a feed-forward back-propagation network in an artificial neural network (ANN) system. In order to achieve the objective, a series of wear trials is conducted in which ten sensory perceptions (clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathable, and thermal) and overall clothing comfort (comfort) are rated by twenty-two professional athletes in a controlled laboratory. They are asked to wear four different garments in each trial and rate the sensations above during a 90-minute exercising period. The scores are were input into five different feed-forward back-propagation neural network models, consisting of six different numbers of hidden and output transfer neurons. Results showing a good correlation between predicted and actual comfort ratings with a significance of p < 0.001 for all five models indicate overall comfort performance is predictable with neural networks, particularly models with log sigmoid hidden neurons and pure linear output neurons. Models with a single log sigmoid hidden layer with fifteen neurons or three hidden layers, each with ten log sigmoid hidden neurons, are able to produce better predictions than the other models for this particular data set in the study.