4.6 Article

Recognition of human mood, alertness and comfort under the influence of indoor lighting using physiological features

Journal

BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 89, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2023.105661

Keywords

Light environment; Non -visual effects; Visual comfort; Physiological measurement; Ensemble learning

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Light has both visual and non-visual effects on the human body, and illuminance and correlated colour temperature (CCT) have significant impacts on human mood, alertness, and comfort. The study found that both illuminance and CCT significantly influenced subjective perception. Using continuous physiological data, subjective feelings under the influence of the light environment were evaluated. The research results are important for creating human-centred indoor environments and personalized user experience research.
Light is an essential and vital component of indoor environments, and it has both visual and non-visual effects on the human body. Illuminance and correlated colour temperature (CCT) have been reported to be closely related to human mood, alertness, and comfort. Recent research has focused on subjective feelings, test results, and transient physiological indicators; however, few studies have used continuous physiological data to explore the physiological features along with light parameters associated with subjective feelings. In this paper, we conducted light experiments at six illuminance levels of functional lighting (300, 400, 500, 600, 700, and 800 lx) and four typical CCT levels (3000, 4000, 5000, and 6000 K) to analyse the light effects on users' mood, alertness, and visual comfort. The results showed that both illuminance and CCT significantly affected subjective perception. Notably, subjective feelings under the influence of the light environment were evaluated using the CatBoost method through 41 columns of physiological features extracted from electrocardiogram (ECG), electrodermal activity (EDA), skin temperature (SKT) and blood oxygen saturation (SpO2) data. The average accuracy values of the recognition framework for binary classification proposed in this study were 97.4% for the valence of mood, 95.7% for the arousal of mood, 97.1% for alertness, and 97.3% for visual comfort (all standard deviations were less than 2.5%). The research results can help intelligent buildings sense changes in human feelings and adjust control systems to construct human-centred indoor environments for work and study, as well as provide a reference of physiological features for personalised user experience research.

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