An intelligent optimization method of exercisers' visual comfort assessment in gymnasium

Abstract

Exercisers' visual comfort is an essential factor in successful gymnasium design. Existing research has identified viable indicators of visual comfort and the explained the interaction between humans and the light environment. However, it remains difficult to accurately quantify the impact of the daylight environment on human perception. Given the particularity of exercisers' behavior and activities in gymnasiums, the current general assessment model for exercisers' visual perception is lacking. Taking a university gymnasium in Harbin as a case, this study aimed to establish a computational method for assessing visual comfort from the human-centric perspective via mutual authentication between questionnaire and physiological indices and luminance. An analysis of the questionnaire responses revealed that the synthetical visual evaluation (SVE) was an appropriate visual evaluation index. Machine learning was applied to quantify the correlation between various luminance levels and human perception to assess exercisers' level of visual comfort. Multilayer Perceptron models with the best-fit optimization were selected by artificial neural networks (ANNs) to determine the most optimized visual comfort assessment model. Based on the ANNs, the correlation coefficient between luminance, SVE, and physiological indicator ranged from 85% to 90%. According to the genetic algorithm, the average luminance of the entire field of view (Lfov) was 55–135 cd/m2, the average luminance of the target area (Lt) was 82–375 cd/m2, and the average luminance of the window area (Lw) was 960–1950 cd/m2, for a comfortable visualization.