Presentation
Recurrence Quantification Analysis of Physiological Responses in Mixed Reality Environments: Exploring the Impact of Cognitive Workload
DescriptionAs mixed reality (MR) technologies become increasingly integrated into training, healthcare, and manufacturing, accurately assessing cognitive workload is essential for maintaining performance and preventing mental overload. This study examines the potential of heart rate variability (HRV) analysis using recurrence quantification analysis (RQA) as a non-invasive, real-time indicator of cognitive workload in immersive MR environments. A total of 103 participants performed a manufacturing assembly task in MR while their physiological responses were recorded. Key RQA features such as recurrence rate, determinism, and laminarity showed significant correlations with self-reported workload metrics, including temporal demand, frustration, presence, and situational stress. These findings suggest that non-linear patterns in HRV, as quantified through RQA, effectively reflect the dynamic interplay between cognitive and emotional states during complex MR tasks. The capability to detect these changes in real time facilitates the development of adaptive MR systems that can dynamically adjust task difficulty, pacing, or feedback in accordance with the user's cognitive state. Such systems could enhance user engagement and task performance. This research contributes to the development of data-driven approaches for real-time cognitive workload assessment and highlights the value of integrating physiological monitoring into immersive systems to support adaptive human-machine interaction and personalized training.
Event Type
Lecture
TimeThursday, October 16th12:10pm - 12:30pm CDT
LocationGrand Hall L
Extended Reality