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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Investigation of a real-time change of human eye pupil sizes for visual environmental controls in the workplace environment
This study adopted a series of human subject experiments to estimate each individual’s visual sensations by analyzing pupil sizes and their change patterns based on the human body’s autonomic nervous system. Various ranges of lighting parameters, including illuminance, contrast ratio, and unified glare rating, were generated and controlled while each test subject’s pupil sizes were recorded. The experimental result data were statistically analyzed to identify a relationship between human visual sensations, lighting parameters, and pupil sizes as well. 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 with an environmental design and control system with the help of an advanced sensing device.
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- Award ID(s):
- 1707068
- NSF-PAR ID:
- 10114211
- Date Published:
- Journal Name:
- Indoor air quality and climate
- ISSN:
- 2616-3268
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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Abstract Chimpanzee ( Pan troglodytes ) sclera appear much darker than the white sclera of human eyes, to such a degree that the direction of chimpanzee gaze may be concealed from conspecifics. Recent debate surrounding this topic has produced mixed results, with some evidence suggesting that (1) primate gaze is indeed concealed from their conspecifics, and (2) gaze colouration is among the suite of traits that distinguish uniquely social and cooperative humans from other primates (the cooperative eye hypothesis). Using a visual modelling approach that properly accounts for specific-specific vision, we reexamined this topic to estimate the extent to which chimpanzee eye coloration is discriminable. We photographed the faces of captive chimpanzees and quantified the discriminability of their pupil, iris, sclera, and surrounding skin. We considered biases of cameras, lighting conditions, and commercial photography software along with primate visual acuity, colour sensitivity, and discrimination ability. Our visual modeling of chimpanzee eye coloration suggests that chimpanzee gaze is visible to conspecifics at a range of distances (within approximately 10 m) appropriate for many species-typical behaviours. We also found that chimpanzee gaze is discriminable to the visual system of primates that chimpanzees prey upon, Colobus monkeys. Chimpanzee sclera colour does not effectively conceal gaze, and we discuss this result with regard to the cooperative eye hypothesis, the evolution of primate eye colouration, and methodological best practices for future primate visual ecology research.more » « less
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Background In Physical Human–Robot Interaction (pHRI), the need to learn the robot’s motor-control dynamics is associated with increased cognitive load. Eye-tracking metrics can help understand the dynamics of fluctuating mental workload over the course of learning.
Objective The aim of this study was to test eye-tracking measures’ sensitivity and reliability to variations in task difficulty, as well as their performance-prediction capability, in physical human–robot collaboration tasks involving an industrial robot for object comanipulation.
Methods Participants (9M, 9F) learned to coperform a virtual pick-and-place task with a bimanual robot over multiple trials. Joint stiffness of the robot was manipulated to increase motor-coordination demands. The psychometric properties of eye-tracking measures and their ability to predict performance was investigated.
Results Stationary Gaze Entropy and pupil diameter were the most reliable and sensitive measures of workload associated with changes in task difficulty and learning. Increased task difficulty was more likely to result in a robot-monitoring strategy. Eye-tracking measures were able to predict the occurrence of success or failure in each trial with 70% sensitivity and 71% accuracy.
Conclusion The sensitivity and reliability of eye-tracking measures was acceptable, although values were lower than those observed in cognitive domains. Measures of gaze behaviors indicative of visual monitoring strategies were most sensitive to task difficulty manipulations, and should be explored further for the pHRI domain where motor-control and internal-model formation will likely be strong contributors to workload.
Application Future collaborative robots can adapt to human cognitive state and skill-level measured using eye-tracking measures of workload and visual attention.
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