Thesis Description & Objectives
Workplace stress in corporate and office environments is an increasingly critical issue, with significant implications for employee wellbeing, productivity, and long-term mental health. Existing stress management solutions are often generic, reactive, and detached from the actual work context, providing limited support when it is most needed. At the same time, Extended Reality (XR) technologies offer new opportunities to create immersive, adaptive, and context-aware interventions. However, their potential for real-time stress detection and mitigation in workplace settings remains largely unexplored.
This thesis aims to investigate how XR technologies can be leveraged to monitor, detect, and actively reduce stress in office and corporate environments. The focus will be on integrating multimodal physiological and behavioral signals to enable personalized, real-time interventions that support employee wellbeing. Particular attention will be devoted to understanding how adaptive XR experiences can respond dynamically to users’ internal states and environmental conditions.
The student will design and develop a system capable of continuously assessing stress levels through biofeedback data, such as heart rate variability, skin conductance, gaze behavior, and posture. Based on these signals, the system will trigger adaptive XR interventions tailored to the user’s current condition. These interventions may include immersive Virtual Reality relaxation scenarios, guided breathing and mindfulness exercises, and Mixed Reality overlays designed to reduce cognitive load and promote recovery during work activities. Emphasis will be placed on real-time adaptation, personalization strategies, and the seamless integration of interventions into everyday workflows.
The developed solution will be evaluated through user studies combining objective physiological measures with subjective assessments of perceived stress, recovery, and overall wellbeing. The analysis will consider the effectiveness, usability, and acceptability of XR-based interventions in realistic workplace scenarios.
The expected outcome of this research is the definition of novel approaches for technology-driven stress management in professional environments. The results are expected to contribute to the design of intelligent, adaptive XR systems for corporate wellness programs, providing guidelines and best practices for delivering timely, personalized, and effective stress reduction interventions in the workplace.
References:
Lakmal Meegahapola, Marios Constantinides, Zoran Radivojevic, Hongwei Li, Michael Eggleston, and Daniele Quercia. 2026. Stress Mindset Matters: Rethinking Mental Stress Detection with Multimodal Wearable Sensors. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, Article 1026, 1–26. https://doi.org/10.1145/3772318.3791340
