Physiologically adaptive systems across the Mixed Reality continuum

Chiossi, Francesco (2024): Physiologically adaptive systems across the Mixed Reality continuum. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik

Abstract

As Mixed Reality (MR) technologies progressively enter our daily lives, blending digital information seamlessly with our physical world, they unlock new potential for enhancing productivity and engagement across various activities, from sketching to typing and object manipulation. However, this fusion of virtual and physical elements also elevates the complexity of our visual environments, challenging our capacity to navigate and process an increasingly congested information space without becoming overwhelmed. This thesis contends that the dynamic nature of MR systems, which blurs the lines between the real and virtual worlds, necessitates the development of adaptive MR environments. Such environments are designed to intelligently modulate and tailor content in real-time, responding to users’ cognitive states and environmental contexts to optimize user engagement and minimize cognitive strain. Centered on the premise that effective interaction within these richly blended settings hinges on users’ ability to manage attentional resources efficiently, this research explores the integration of physiological computing into MR. By leveraging real-time monitoring of physiological signals, MR systems can gain implicit insights into the user’s attentional state and cognitive load, enabling them to adapt dynamically to support the user’s immediate needs and objectives. By examining how various degrees of virtuality affect users’ engagement, attentional allocation, and workload, this thesis systematically investigates the potential of adaptive MR systems to enhance user experience without compromising productivity. The investigation extends to designing, evaluating, and implementing physiological computing systems within MR environments, offering novel insights into supporting task engagement and managing attentional load. Moreover, it tackles the technical challenges of embedding physiological sensors within MR hardware, proposing a groundbreaking approach to unobtrusive user state monitoring. This thesis makes significant strides in bridging the gap between physiological computing and MR, laying the groundwork for future research in ubiquitous computing, pervasive computing, and affective computing. Its findings highlight the critical role of interdisciplinary collaboration in understanding and realizing implicit interaction in adaptive MR systems, where intelligent environments enhance our daily lives by being responsive, intuitive, and seamlessly integrated.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Keywords: Human-Computer Interaction, Physiological Computing, Mixed Reality, Intelligent User Interfaces, Artificial Intelligence
• Themengebiete: 000 Allgemeines, Informatik, Informationswissenschaft ; 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
• Fakultäten: Fakultät für Mathematik, Informatik und Statistik
• Sprache der Hochschulschrift: Englisch
• Datum der mündlichen Prüfung: 11. November 2024
• 1. Bericht­erstatter:in: Schmidt, Albrecht