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BackgroundHead-mounted displays (HMDs) and virtual reality (VR) have been frequently used in recent years, and a user’s experience and computation efficiency could be assessed by mounting eye-trackers. However, in addition to visually induced motion sickness (VIMS), eye fatigue has increasingly emerged during and after the viewing experience, highlighting the necessity of quantitatively assessment of the detrimental effects. As no measurement method for the eye fatigue caused by HMDs has been widely accepted, we detected parameters related to optometry test. We proposed a novel computational approach for estimation of eye fatigue by providing various verifiable models.ResultsWe implemented three classifications and two regressions to investigate different feature sets, which led to present two valid assessment models for eye fatigue by employing blinking features and eye movement features with the ground truth of indicators for optometry test. Three graded results and one continuous result were provided by each model, respectively, which caused the whole result to be repeatable and comparable.ConclusionWe showed differences between VIMS and eye fatigue, and we also presented a new scheme to assess eye fatigue of HMDs users by analysis of parameters of the eye tracker.

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Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic.

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Across the world, there are approximately 253 million people with vision impairments, and assistive devices have constantly been in demand. Advanced research has led to the development of numerous assistive devices for blind people and visually impaired people (VIP) to improve their quality of life. An overview of these different types of assistive devices such as canes, glasses, hats and gloves is presented in this survey. A FCBPSS (F: function, C: context, B: behaviour, P: principle, S: state, S: structure) architecture of visual impairment assistance system is preliminarily proposed to allow other researchers to design the assistive devices with the good experience and the high performance for blind people and VIPs in the future. As VIPs and blind people may have different behaviour patterns, a criterion for classifying different types of vision impairments is presented. Subsequently, we classify the substitutive senses for visual perception into five categories: vision enhancement, audition, somatosense, visual prosthesis, and olfactory and gustation. Two commonly used feedback forms, namely audition and vibration, are elaborated.Based on literature survey, we also present a summary prospective of the development of assistive devices: add more sensing and feedback modules, use the knowledge of perception mechanism and behaviour pattern as the design guideline and design more reliable validation experiments.

Motion in a distorted virtual 3D space may cause visually induced motion sickness. Geometric distortions in stereoscopic 3D can result from mismatches among image capture, display, and viewing parameters. Three pairs of potential mismatches are considered, including 1) camera separation vs. eye separation, 2) camera field of view (FOV) vs. screen FOV, and 3) camera convergence distance (i.e., distance from the cameras to the point where the convergence axes intersect) vs. screen distance from the observer. The effect of the viewer’s head positions (i.e., head lateral offset from the screen center) is also considered. The geometric model is expressed as a function of camera convergence distance, the ratios of the three parameter-pairs, and the offset of the head position. We analyze the impacts of these five variables separately and their interactions on geometric distortions. This model facilitates insights into the various distortions and leads to methods whereby the user can minimize geometric distortions caused by some parameter-pair mismatches through adjusting of other parameter pairs. For example, in postproduction, viewers can correct for a mismatch between camera separation and eye separation by adjusting their distance from the real screen and changing the effective camera convergence distance.

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