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1. Investigation of a real-time change of human eye pupil sizes for visual environmental controls in the workplace environment

   Choi, J.; Lin, X. (January 2018, Indoor air quality and climate)

   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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2. A Personal Visual Comfort Model: Predict Individual’s Visual Comfort Using Occupant Eye pupil Size and Machine Learning

   https://doi.org/10.1088/1757-899X/609/4/042097  

   Cen, Lingkai; Choi, Joon-Ho; Yao, Xiaomeng; Gil, Yolanda; Narayanan, Shrikanth; Pentz, Maryann (September 2019, IOP conference series)

   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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3. Investigation of the physiological differences in the immersive virtual reality environment and real indoor environment: Focused on skin temperature and thermal sensation

   Yeom, D.; Choi, J. (January 2019, Building and environment)

   The goal of this research is to investigate and determine whether the effect of an IVE condition on an occupant's environmental sensations and physiological responses is different from the effect of a real environmental condition in the indoor environment. The research included a series of human subject experiments, with 16 participants in an environmental chamber. A thermal quality condition was selected as a primary environmental parameter, based on current IEQ-relevant studies. While the ambient thermal condition was gradually changed from 20 °C to 30 °C, the participants were asked to report their overall thermal sensations. Their skin temperatures were also continuously measured to collect physiological signal information in real time. The results of this experimental study revealed that the participants mostly generated higher skin temperature at the selected seven skin areas. Their reported thermal sensations were significantly higher in the IVE condition, than in the real environment, showing a difference of 12%. 

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4. Cybersickness in Virtual Reality: Examining the Influence of the Virtual Environments on Sex Susceptibility

   https://doi.org/10.1145/3366344.3366628  

   Curry, Christopher (November 2019, SIGGRAPH Asia 2019 Doctoral Consortium)

   The auspicious future of VR could be thwarted by cybersickness. A factor known to influence susceptibility is sex, with females often experiencing higher incidences. A mitigation strategy is to identify individuals who are more sensitive to cybersickness, such that interventions can be implemented before the onset of subjective symptoms. Such an approach could use predictive models that compare a user’s online kinematic body sway and physiological characteristics to data from individuals that reported cybersickness. If such predictive models can be developed, then one approach is altering the virtual environment (VE) based on this real-time data. The benefit of adjusting the VE is that it permits a susceptible individual to use the VR device with a reduction in adverse symptoms. One way to alter the VE is by manipulating optic flow, which can be described as the perceived visual motion of objects that are generated through an observer’s movements. Optic flow can be increased by increasing the level of details in the VE. That is to say, visual displays that contain a lot of details often give rise to stronger subjective sensations of movement. Thus, if the level of details in the VE is reduced, then this may reduce cybersickness reports. 

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5. Adjusting Transparency Toward Optimizing Face Appearance in Optical See-Through Augmented Reality

   https://doi.org/10.2352/J.Percept.Imaging.2024.7.000404  

   Herbeck, Sofie R; Murdoch, Michael J; Thorstenson, Christopher A (October 2024, Journal of Perceptual Imaging)

   Rogowitz, Bernice E; Pappas, Thrasyvoulos N (Ed.)

   Augmented reality (AR) combines elements of the real world with additional virtual content, creating a blended viewing environment. Optical see-through AR (OST-AR) accomplishes this by using a transparent beam splitter to overlay virtual elements over a user’s view of the real world. However, the inherent see-through nature of OST-AR carries challenges for color appearance, especially around the appearance of darker and less chromatic objects. When displaying human faces—a promising application of AR technology—these challenges disproportionately affect darker skin tones, making them appear more transparent than lighter skin tones. Still, some transparency in the rendered object may not be entirely negative; people’s evaluations of transparency when interacting with other humans in AR-mediated modalities are not yet fully understood. In this work, two psychophysical experiments were conducted to assess how people evaluate OST-AR transparency across several characteristics including different skin tones, object types, lighting conditions, and display types. The results provide a scale of perceived transparency allowing comparisons to transparency for conventional emissive displays. The results also demonstrate how AR transparency impacts perceptions of object preference and fit within the environment. These results reveal several areas with need for further attention, particularly regarding darker skin tones, lighter ambient lighting, and displaying human faces more generally. This work may be useful in guiding the development of OST-AR technology, and emphasizes the importance of AR design goals, perception of human faces, and optimizing visual appearance in extended reality systems. 

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