Lighting is the most crucial factor impacting an occupants’ visual comfort in a building environment. However, most prevailing current lighting guidelines deriving from empirical values are designed primarily for paper-based tasks, rather than computer-based. In many cases, present guidelines have been reported that there is a limitation to meet the needs for a user’s new task types. Above all, existing technical tools also have a limited function to evaluate a user’s real-time visual perception which can be applied as an indicator to control a building lighting system. This research estimated each individual participant's visual sensations by analyzing pupil sizes and their change patterns since the human body have the physiological regulation ability which naturally minimizes the adverse effects of the surrounding environment on the human body. This study adopted a series of human subject experiments which were performed in an environmental chamber of USC. Based on a computer-based task which are most commonly performed in current offices, various ranges of ambient lighting parameters, including luminance (cd/m2), illuminance (lux), contrast ratio, and UGR, were generated and controlled while each subject’s pupil sizes were recorded. The experimental result data were statistically analyzed to identify a relationship between human visual sensations, lighting parameters, and also pupil sizes by ethnic origin and myopia condition. The research outcomes showed the potential use of pupil sizes for estimating an individual’s visual sensation, and confirmed the principle as an applicable technology to integrate an environmental design and control system with the help of a real-time sensing device.
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A Personal Visual Comfort Model: Predict Individual’s Visual Comfort Using Occupant Eye pupil Size and Machine Learning
Lighting, as a significant component of indoor environment quality, was found to be a primary contributor to deficient indoor environments in today’s workplace. This resulted from the fact that current guidelines are derived from empirical values and neglect the prevalence of computer-based tasks in current offices. A personal visual comfort model was designed to predict the degree of an individual’s visual comfort, as a way of evaluating the indoor lighting of the environment. Development of the model relied on experimental data, including individual eye pupil sizes, visual sensations, and visual satisfaction in response to various illuminance levels used for tests of six human subjects. The results showed that (1) A personal comfort model was needed, (2) the personal comfort model produced a median accuracy of 0.7086 for visual sensation and 0.65467 for visual satisfaction for all subjects; (3) To develop a prediction model for the sample group, the Support Vector Machine algorithm,, outperformed the Logistic Regression and the Gaussian Naïve Bayes in terms of prediction accuracy. It was concluded that, a personal visual comfort model can use a building’s occupant’s eye pupil size to generate an accurate prediction of that occupant’s visual sensations and visual satisfaction that can, therefore, be applied with lighting control to improve the indoor environment and energy use in that building.
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- Award ID(s):
- 1707068
- NSF-PAR ID:
- 10183466
- Date Published:
- Journal Name:
- IOP conference series
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 1757-8981
- Page Range / eLocation ID:
- 1025-1030
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1088/1757-899X/609/4/042097
