Digital Wellbeing

Our Mission

The Digital Wellbeing Research Group acts at the intersection between digital technologies and human wellbeing. We work across disciplines, collaborating with clinical specialists, social scientists, and technical experts to tackle issues that affect the most vulnerable sectors of society. We develop our own tools, such as sensor-embedded, context-rich eye-tracking, to better understand large scale issues in physical and mental health.

Our goal is to contribute evidence-based, socially and technologically innovative concepts and solutions, and to inform policy on a regional and wider EU level.

Projects

EyeQTrack - Quantitative Eye-Tracking Analytics for Adaptive XR Training & Rehabilitation in Healthcare

A self-adaptive eXtended reality environment for training and therapeutic purposes.

XR2Resilience

An Extended Reality training program to improve the resilience of health care workers.

Umbrello

The interdisciplinary project UmBrello investigates the feasibility of a communications- and service platform for elderly citizens in rural areas.

BRELOMATE 2

BRELOMATE 2 aims at creating a user friendly and inexpensive centerpiece of an information, communication and gaming platform for elderly people.

BRELOMATE

The project BRELOMATE aims at providing socially isolated senior citizens with all the possibilities offered by existing information services and social networks on the Internet.

Smart Companion

Using everyday low-cost robot sensors and smart speech recognition in assistance services for elderly people

Publications

Download Bibtex for all results

Vulpe-Grigorasi, A., Gollan, B., & Leung, V. (2024). Assessing Cognitive Load in Distraction and Task Switching: Implications for Developing Realistic Clinical XR Training. 41st Computer Graphics International Conference, CGI 2024, Geneva, Switzerland, July 1–5, 2024, Proceedings, Part II. CGI Computer Graphics International 2024, Geneva Switzerland. https://link.springer.com/book/9783031820205

Vulpe-Grigorasi, A., Kren, Z., Slijepčević, D., Schmied, R., & Leung, V. (2024). Attention performance classification based on eye tracking and machine learning. Informatics 2024 Proceedings. IEEE International Scientific Conference on Informatics, Poprad Slovakia.

Bibtex

Vulpe-Grigorasi, A. (2023). Cognitive load assessment based on VR eye-tracking and biosensors. Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, 589–591. https://doi.org/10.1145/3626705.3632618

Vulpe-Grigorasi, A. (2023). Multimodal machine learning for cognitive load based on eye tracking and biosensors. 2023 Symposium on Eye Tracking Research and Applications, 1–3. https://doi.org/10.1145/3588015.3589534

Leung, V., Simone Hofbauer, Leonhartsberger, J., Kee, C., Liang, Y., & Schmied, R. (2022, 05). Influence of education systems on children’s visual behaviours as an environmental risk factor for myopia: a quantitative analysis with LIDAR-sensor tracking in classrooms. 18th International Myopia Conference, Rotterdam.