We present a survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarize multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardized experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Mobile health apps can help to change health-related behaviors and manage chronic conditions in patients with cardiovascular diseases (CVD) and diabetes mellitus, but a certain level of health literacy and eHealth literacy may be needed. Objective: To predict mobile health app use in individuals with CVD and/or diabetes, and to predict perceived effectiveness of health apps amongst app users. Methods: Population-based online survey (N=1500) among Germans, aged 35 years and older with CVD, diabetes or both. Three subgroups were examined: 1) individuals with CVD (n=1325); 2) individuals with diabetes (n=681); and 3) individuals with CVD and diabetes (n=524). Sociodemographics, health behaviors, CVD and/or diabetes, health and eHealth literacy, characteristics of health app use, and characteristics of apps themselves were assessed by questionnaires. Linear and logistic regression models were applied. Results: Overall, patterns of factors predicting health app use were comparable in individuals with CVD or diabetes or both. Across subgroups, about every fourth patient reported using apps for health-related purposes with physical activity and weight loss being the most prominent target behaviors. Health app users were younger, more likely to be female (except in those with CVD and diabetes combined), better educated, and reported more physical activity App users had higher eHealth literacy than non-users. Those users who perceived the app to have a greater effectiveness on their health behaviors tended to be more health and eHealth literate and rated the app to use more behavior change techniques (BCTs). Conclusions: There are health and literacy-related disparities in the access to health app use. Apps containing more BCTs had higher perceived effect on people’s health and app developers should take the complexity of needs into account. Further, eHealth literacy appears to be a requirement to use health apps successfully, which should be considered in health education strategies to improve health in patients with CVD and diabetes.

Common speech quality evaluation methods rely on self-reported opinions after perceiving test stimuli. Whereas these methods-when carefully applied-provide valid and reliable quality indices, they provide little insight into the processes underlying perception and judgment. In this paper, we analyze the performance of electroencephalography (EEG) for indicating different types of degradations in speech stimuli. We show that a certain EEG technique, event-related-potentials (ERP) analysis, is a useful and valid tool in quality research. Three experiments are reported which show that quality degradations can be monitored in conscious and presumably non-conscious stages of processing. Potential and limitations of the approach are discussed and lines of future research are drawn.

Using less bandwidth on the one hand and delivering high quality content on the other hand is one of the big goals for video service providers. Standardized rating tests are commonly used to quantify audiovisual quality. In order to better understand the neuronal processes underlying these quality ratings physiological measures may provide insights. This paper shows results of a series of studies using a physiological measurement, namely electroencephalography (EEG), combined with standard rating methods. The experiments follow an incremental approach in stimuli selection, ranging from purely visual to audiovisual stimuli. Results show that EEG is a feasible complement measurement technique to assess audiovisual quality as we achieve high correlation values between subjective and physiological data consistently through all experiments.

Objective. Assessing speech quality perception is a challenge typically addressed in behavioral and opinion-seeking experiments. Only recently, neuroimaging methods were introduced, which were used to study the neural processing of quality at group level. However, our electroencephalography (EEG) studies show that the neural correlates of quality perception are highly individual. Therefore, it became necessary to establish dedicated machine learning methods for decoding subject-specific effects. Approach. The effectiveness of our methods is shown by the data of an EEG study that investigates how the quality of spoken vowels is processed neurally. Participants were asked to indicate whether they had perceived a degradation of quality (signal-correlated noise) in vowels, presented in an oddball paradigm. Main results. We find that the P3 amplitude is attenuated with increasing noise. Single-trial analysis allows one to show that this is partly due to an increasing jitter of the P3 component. A novel classification approach helps to detect trials with presumably non-conscious processing at the threshold of perception. We show that this approach uncovers a non-trivial confounder between neural hits and neural misses. Significance. The combined use of EEG signals and machine learning methods results in a significant ‘neural’ gain in sensitivity (in processing quality loss) when compared to standard behavioral evaluation; averaged over 11 subjects, this amounts to a relative improvement in sensitivity of 35%.

Nowadays, Virtual Reality (VR) technology can be potentially used everywhere through wearable head-mounted displays. Nevertheless, it is still uncommon to see VR devices used in public settings. In these contexts, unaware bystanders in the surroundings might influence the User Experience (UX) and create concerns about the social acceptability of this technology. The user acts in a Social Environment (SE), characterized by surrounding people's number, proximity, and behavior. Simultaneously, VR applications often require a different degree of interactivity concerning body movements and controllers interaction. In this paper, the influence of Social Environments, and degree of interactivity on User Experience and social acceptability is investigated. Four Social Environments were simulated employing 360° Videos, and two VR games developed with two levels of interactivity. Results showed a statistically significant influence of Social Environments on Overall UX as well as Public VR, Interaction, Isolation, Privacy and Safety acceptability, and of the degree of interactivity on Presence, Valence, Arousal, Overall UX, UX Hedonic quality, and Safety acceptability. Findings indicate that Social Environments and degree of interactivity should be taken into account while designing VR applications.