This work-in-progress paper presents findings from a pilot study (N = 30), conducted as part of the Horizon Europe AMPLIFY project, investigating physiological responses to music. Participants listened to two counterbalanced musical excerpts while heart rate, electrodermal activity (EDA) and skin temperature were recorded via EmotiBit™ wearable sensors. Of 489 statistically significant individual deviations from baseline (Bonferroni-corrected Mann-Whitney U), 33 were shared across multiple participants. To check whether these occurred at the same instant, temporal analysis of the shared deviations identified 82 different episodes in which multiple participants deviated simultaneously. Comparing audio features during these episodes to the remainder of each musical excerpt yielded 27 Bonferroni-significant associations (Cohen's d up to 1.62). Broadband EDA and skin conductance response frequency emerged as the most responsive physiological measures, showing strong correlations with spectral brightness and timbral features of the musical passages.

This paper investigates immersive Extended Reality (XR) broadcasting as a means of enhancing audience experience within production-realistic workflows. An XR broadcast system was iteratively refined through broadcaster-in-the-loop evaluations to improve usability and production stability. A controlled audience study then compared an immersive XR broadcast with a conventional 2D Twitch stream presenting identical content. Participants in the XR condition viewed the broadcast using head-mounted displays within a shared virtual studio, while participants in the 2D condition viewed the stream on desktop displays while physically co-located in the same classroom environment, providing a strong baseline that preserved interpersonal awareness.

Auditory attention is a fundamental cognitive process essential for effective communication and interaction. Auditory stimuli are often accompanied by distractors that can significantly impact task performance by means of reducing attention. This study investigates the influence of auditory and audiovisual distractors on an auditory attention task within a Virtual Reality classroom. As part of the user evaluation, participants had to listen to two short stories. They performed two tasks: (i) “DISTRACTORS”, where participants had to identify an auditory or an audiovisual stimulus and (ii) “KEYWORDS,” where participants had to identify a specific keyword in the story by pressing a button on the controller. During the experiment, the participants’ physiological (e.g. skin conductance levels, gaze data, etc.) and subjective data (i.e. questionnaires) were collected. The results revealed that the interval between the presentation of the keyword and the presentation of the distractors impacted task performance by negatively affecting auditory attention. Also, it was observed that the time spent looking at the speaker telling the story positively correlated with task performance, whereas the time spent looking at distractors was found to be negatively correlated with task performance. Finally, this study gives insights into how physiological metrics can be used to infer auditory attention in VR experiences.

The current definition of quality of experience (QoE) designates delight and annoyance as diametrically opposing indicators of the degree of fulfilment of an application, service or system user's pragmatic and hedonic needs and expectations. However, these inherited emotion terms are rarely used to describe emotions of equal amounts of arousal or opposing amounts of valence in the literature. This work assesses the significance of this asymmetry to the definition of QoE by determining the utility of emotion terms to communicate the emotion component of QoE. This was done in the context of a QoE evaluation of augmented reality training instruction formats. Correlates were sought between various measures of emotional state. This included physiological ratings, facial expressions and eye gaze. Emotional state was subjectively reported using three distinct methods: self-assessment manikin questionnaire; 2D emotion space terms; and open-ended terms. Regression analysis showed multiple significant correlations between implicit and explicit metrics, but not to the emotion terms used by the participants. This calls into question the utility of such vaguely understood terms. The use of more symmetrically opposing emotions in the definition of QoE may benefit consensual interdisciplinary communication.

Augmented reality (AR) has significant potential as a training platform. The pedagogical purpose of training is learning or transfer. Learning is the acquisition of an ability to perform a procedure as taught while transfer involves generalising that knowledge to similar procedures in the same domain. Quality of experience (QoE) concerns the fulfilment of the application, system or service user's pragmatic and hedonic needs and expectations. Learning or transfer fulfil the AR trainee's pragmatic needs. Training instructions can be presented in procedural, and example formats. Procedural instructions tell the trainee what to do while examples show the trainee how to do it. These two different instruction formats can influence learning, transfer, and hardware resource availability differently. The AR trainee's hedonic needs and expectations may be influenced by the impact of instruction format resource consumption on system performance. Efficient training efficacy is a design concern for mobile AR training applications. This work aims to inform AR training application design by evaluating the influence of procedural and example instruction formats on AR trainee QoE.

Augmented reality (AR) is a key technology to enhance worker effectiveness during increasing automation of repetitive jobs in the workplace. AR will achieve this by assisting the user to successfully perform complex and frequently changing procedures. The design of AR applications for these roles is critical to their acceptability and utility. User quality of experience (QoE) will inform these design decisions. A user arrives at a quality judgment upon post-experience reflection of their degree of delight or annoyance relating to the degree of fulfilment of pragmatic and hedonic needs and expectations of the medium under consideration. QoE researchers have largely depended upon post-experience subjective reports to determine the user's QoE. Subjective reports have been shown to be biased by primacy, recency and maxima of experience stimuli. Recent research involves the identification of implicit metrics that can be used to determine user QoE continuously during a multimedia experience. This work evaluates head rotation frequency as an objective metric of user QoE. The literature shows that emotion is expressed in the frequency of a person's head rotation around three axes of movement (pitch, yaw and roll). Low frequency head rotation has been shown to include expression of happy emotion while high frequency exclusively expresses anger emotion. These emotions are analogous to those reflected on by a user during the quality formation process. This demo paper analyses the amount of high frequency head rotation exhibited by the user upon task completion using an AR procedure assistance application or a paper-based control. An optimal Rubik's Cube solving AR application was used as a proof of concept for AR-based procedure assistance. Preliminary results showed that the AR environment yielded higher task success rates and significantly shorter task completion durations. The AR users exhibited significantly lower amplitudes of anger frequencies in their head rotations than the control group.

Augmented reality (AR) can support a user in performing an expert task by overlaying real world objects with the domain specific information required to complete the task. Understanding how users can process and use such information is very important for informing the design of AR technologies and applications. In this paper, the results of a quality of experience (QoE) evaluation of an AR application for the task of solving a Rubik's Cube are presented. The Rubik's Cube was selected based on its familiarity and the expertise needed to solve it unaided. An empirical approach was taken to identify the QoE features that affect the usability and utility of an AR head-mounted display (HMD) compared with paper-based instruction. The QoE evaluation methodology involved the capture and analysis of implicit and explicit QoE metrics. The utility (in terms of performance) of each mode of instruction was objectively measured using: (a) cube completion success rates; and (b) time-to-completion. The implicit metrics of electrodermal activity (EDA), skin temperature, heart rate and the novel use of facial action units (AUs) were recorded to infer emotional state during the task completion. Finally, with respect to explicit metrics, the test subjects completed a Likert scale questionnaire post the experience to subjectively report QoE as well as a self-assessment manikin (SAM) questionnaire to self-report emotional state upon task completion. The results show that AR yielded higher success rates and significantly lower time-to-completion rates. The AR group explicitly reported higher levels of positive valance (affective state) than the paper-based group. The physiological data showed that the AR group were less stressed (via EDA) than the paper-based group. Finally, analysis of the AU data reflected a greater than chance (total: 21.85%) accuracy when predicting affective state based on SAM questionnaires as ground-truth.