The usage of virtual avatars in healthcare applications has become widely popular; however, certain critical aspects, such as social distancing and avatar size, remain insufficiently explored. This research investigates user experience and preferences when interacting with a healthcare application utilizing virtual avatars displayed in different sizes. For our study, we had 23 participants interacting with five different avatars (a human-size avatar followed by four, respectively, smaller avatars in a randomized order) varying in size, projected on a wall in front of them. The avatars were fully integrated with an artificial intelligence chatbot to make them conversational. Users were asked to rate the usability of the system after interacting with each avatar and complete a survey regarding trust and an additional questionnaire on social presence. The results of this study show that avatar size significantly influences the perceived attractiveness and perspicuity with the medium-sized avatars having received the highest ratings. Social presence correlated strongly with stimulation and attractiveness, suggesting that an avatar’s visual appeal and interactivity influenced user engagement more than its physical size. Additionally, we observed a tendency for gender-specific differences on some of the UEQ+ scales, with male participants tending to prefer human-sized representations, while female participants slightly favored smaller avatars. These findings highlight the importance of avatar design and representation in optimizing user experience and trust in virtual healthcare environments.

While current Quality of Experience (QoE) formation models recognize the impact of user states on perception, they often overlook the subjective nuances and individual variations in these experiences. We discuss the integration of both situation-dependent and independent user states, show how dependent states are influenced by varying multimedia content quality, and reflect back into the QoE assessment. To address this gap, we conducted a comprehensive within-subjects lab study (N=92) in a video-telephony setting, employing variables representative of both dependent and independent states, such as affective states, social relationship, sympathy, and bodily needs. Dependent states were assessed per trial and independent states before or after the experiment. Our study investigates two key questions: How does video-telephony call quality influence user-dependent states, and how do both dependent and independent states collectively impact QoE ratings? Our findings reveal a substantial influence of call quality on user emotional states, underscoring the importance of considering these factors in QoE assessments. Moreover, a structural equation model comparison favored the dependent+independent state structure model, highlighting the significant impact of both independent and preceding dependent states on QoE ratings. This research advances QoE models by incorporating a more nuanced mental model of user states, leading to more personalized and accurate multimedia assessment tools, potentially enhancing users’ degree of delight or annoyance and engagement.

The collaboration in co-located shared environments has sparked an increased interest in immersive technologies, including Augmented Reality (AR). Since research in this field has primarily focused on individual user experiences in AR, the collaborative aspects within shared AR spaces remain less explored, and fewer studies can provide guidelines for designing this type of experience. This article investigates how the user experience in a collaborative shared AR space is affected by divergent perceptions of virtual objects and the effects of positional synchrony and avatars. For this purpose, we developed an AR app and used two distinct experimental conditions to study the influencing factors. Forty-eight participants, organized into 24 pairs, participated in the experiment and jointly interacted with shared virtual objects. Results indicate that divergent perceptions of virtual objects did not directly influence communication and collaboration dynamics. Conversely, positional synchrony emerged as a critical factor, significantly enhancing the quality of the collaborative experience. On the contrary, while not negligible, avatars played a relatively less pronounced role in influencing these dynamics. These findings can potentially offer valuable practical insights, guiding the development of future collaborative AR/VR environments.

The current research evaluates user experience and preference when interacting with a patient-reported outcome measure (PROM) healthcare application displayed on a single tablet in comparison to interaction with the same application distributed across two tablets. We conducted a within-subject user study with 43 participants who engaged with and rated the usability of our system and participated in a post-experiment interview to collect subjective data. Our findings showed significantly higher usability and higher pragmatic quality ratings for the single tablet condition. However, some users attribute a higher level of presence to the avatar and prefer it to be placed on a second tablet.

Preservation of private user data is of paramount importance for high Quality of Experience (QoE) and acceptability, particularly with services treating sensitive data, such as IT-based health services. Whereas anonymization techniques were shown to be prone to data re-identification, synthetic data generation has gradually replaced anonymization since it is relatively less time and resource-consuming and more robust to data leakage. Generative Adversarial Networks (GANs) have been used for generating synthetic datasets, especially GAN frameworks adhering to the differential privacy phenomena. This research compares state-of-the-art GAN-based models for synthetic data generation to generate time-series synthetic medical records of dementia patients which can be distributed without privacy concerns. Predictive modeling, autocorrelation, and distribution analysis are used to assess the Quality of Generating (QoG) of the generated data. The privacy preservation of the respective models is assessed by applying membership inference attacks to determine potential data leakage risks. Our experiments indicate the superiority of the privacy-preserving GAN (PPGAN) model over other models regarding privacy preservation while maintaining an acceptable level of QoG. The presented results can support better data protection for medical use cases in the future.