Abstract
As mixed reality (MR) technologies become increasingly integrated into real-world applications (e.g., training, healthcare, and manufacturing), accurately assessing cognitive workload is essential for maintaining performance and preventing mental overload. This study investigates the potential of heart rate variability (HRV), evaluated through recurrence quantification analysis (RQA), as a non-invasive, real-time indicator of cognitive workload in immersive mixed reality (MR) environments. A total of 103 participants performed a manufacturing assembly task in an MR environment while their physiological responses were recorded. Key RQA features such as recurrence rate (REC), determinism (DET), laminarity (LAM), and average diagonal length (ADL) showed significant correlations with self-reported workload metrics, including temporal demand, frustration, presence, and situational stress. These findings suggest that non-linear patterns in root mean square of successive differences (RMSSD) and standard deviation of normal-normal beats (SDNN), as quantified through RQA, reflect the dynamic interplay between cognitive and emotional states during complex MR tasks. 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.
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