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Explicit and Implicit Measures in Video Quality Assessment
... Its main drawback is that it measures the subjective judgment of viewers and, hence, does not provide information about the processes that underlie the forming of the said judgment. To this end, physiological methods such as electroencephalogram (EEG) have been helpful [5]. ...
The omnidirectional nature of Virtual Reality (VR)
content provides an immersive experience for the viewer. At the
same time, VR content relies heavily on the quality of the video
to deliver an immersive experience. This study investigates the
effect of video quality degradation on aspects of the viewer’s
quality of experience (QoE) via subjective (i.e., a questionnaire)
and objective (i.e., electroencephalogram) methods. We measured
the viewer’s experience of watching a five-minute-long 6DoF VR
movie in four video quality versions. Analysis of the questionnaire
data showed that subjective ratings of the video quality decreased
in parallel with the degradation of the quality. Also, the lower
video quality versions yielded lower sensory immersion and
simulator sickness scores. Finally, analysis of the EEG data
revealed significantly lower parietal and occipital alpha values
for the low video quality versions of the content.
Gaze tracking is a technological discipline that offers an alternative interaction between human and computer. A common solution based on gaze tracking has two elements: a hardware device to obtain data from user’s gaze and a library to process the gathered data. This work makes a formal analysis of previous works in gaze tracking. It presents the most used devices and libraries in gaze tracking solutions in the last 5 years as well as the most important characteristics of each of them. To fulfill this purpose, a search for articles related with eye tracking solutions was carried out in different scientific databases. After that, a classification of results was made, as well as a review of them. In addition, the article offers the areas of IoT in which gaze tracking has had the most influence. Finally, various selection criteria for devices and libraries are offered. These criteria are based on features such as price, technical performance and compatibility.
The aim is to develop an intelligent automatic facial expression recognition and emotion analysis (AFEREA) algorithm that, first, characterizes the time-based raw signals of biosensors in quantitative indicators of the emotional state of the individuals participating in an experiment and, second, compares the emotional reactions across them in terms of intensity and duration. The proposed Statistical Emotion Control (SEC) intelligent algorithm is based on statistical process control (SPC) theory. After representing the individuals’ baseline behaviour in a non-normal I-chart and describing the output per subject in emotional peaks with their corresponding duration in terms of relative cutoffs, SEC uses Poisson c-charts to compare across subjects in terms of the quantity of peaks and binomial p-charts in terms of length of the emotional reactions. To validate the data-driven algorithm, the state-of-the-art iMotions software and its AFFDEX face recognition and emotion analysis algorithm is used to record the individuals while receiving the results of their economic decisions when playing an experimental business game. The SEC intelligent algorithm is proven to be useful to take the raw output of the biosensors, to characterize the intensity and duration of the emotional reactions as well as to compare across subjects by emotion. SEC recognizes “out of control” negative emotions more often (7.25% vs. 2.00%) and positive emotions as often (15.63%) by setting relative cutoffs instead of traditional absolute thresholds. The results show significant pairwise discrepancies among both tested settings in 7.86% of the recorded 560 combinations of emotions and individuals, with a high 43.59% among those timeseries with the maximum recorded value above the traditional threshold of 50.
Background
Herd immunity or community immunity refers to the reduced risk of infection among susceptible individuals in a population through the presence and proximity of immune individuals. Recent studies suggest that improving the understanding of community immunity may increase intentions to get vaccinated.
Objective
This study aims to design a web application about community immunity and optimize it based on users’ cognitive and emotional responses.
Methods
Our multidisciplinary team developed a web application about community immunity to communicate epidemiological evidence in a personalized way. In our application, people build their own community by creating an avatar representing themselves and 8 other avatars representing people around them, for example, their family or coworkers. The application integrates these avatars in a 2-min visualization showing how different parameters (eg, vaccine coverage, and contact within communities) influence community immunity. We predefined communication goals, created prototype visualizations, and tested four iterative versions of our visualization in a university-based human-computer interaction laboratory and community-based settings (a cafeteria, two shopping malls, and a public library). Data included psychophysiological measures (eye tracking, galvanic skin response, facial emotion recognition, and electroencephalogram) to assess participants’ cognitive and affective responses to the visualization and verbal feedback to assess their interpretations of the visualization’s content and messaging.
Results
Among 110 participants across all four cycles, 68 (61.8%) were women and 38 (34.5%) were men (4/110, 3.6%; not reported), with a mean age of 38 (SD 17) years. More than half (65/110, 59.0%) of participants reported having a university-level education. Iterative changes across the cycles included adding the ability for users to create their own avatars, specific signals about who was represented by the different avatars, using color and movement to indicate protection or lack of protection from infectious disease, and changes to terminology to ensure clarity for people with varying educational backgrounds. Overall, we observed 3 generalizable findings. First, visualization does indeed appear to be a promising medium for conveying what community immunity is and how it works. Second, by involving multiple users in an iterative design process, it is possible to create a short and simple visualization that clearly conveys a complex topic. Finally, evaluating users’ emotional responses during the design process, in addition to their cognitive responses, offers insights that help inform the final design of an intervention.
Conclusions
Visualization with personalized avatars may help people understand their individual roles in population health. Our app showed promise as a method of communicating the relationship between individual behavior and community health. The next steps will include assessing the effects of the application on risk perception, knowledge, and vaccination intentions in a randomized controlled trial. This study offers a potential road map for designing health communication materials for complex topics such as community immunity.
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