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PRODID:Linklings LLC
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TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135203Z
LOCATION:Grand Hall L
DTSTART;TZID=America/Chicago:20251016T121000
DTEND;TZID=America/Chicago:20251016T123000
UID:HFESAM_ASPIRE 2025_sess159_LECT688@linklings.com
SUMMARY:Recurrence Quantification Analysis of Physiological Responses in M
 ixed Reality Environments: Exploring the Impact of Cognitive Workload
DESCRIPTION:Khalid Bello and Faisal Aqlan (University of Louisville)\n\nAs
  mixed reality (MR) technologies become increasingly integrated into train
 ing, healthcare, and manufacturing, accurately assessing cognitive workloa
 d is essential for maintaining performance and preventing mental overload.
  This study examines the potential of heart rate variability (HRV) analysi
 s using recurrence quantification analysis (RQA) as a non-invasive, real-t
 ime indicator of cognitive workload in immersive MR environments. A total 
 of 103 participants performed a manufacturing assembly task in MR while th
 eir physiological responses were recorded. Key RQA features such as recurr
 ence rate, determinism, and laminarity showed significant correlations wit
 h self-reported workload metrics, including temporal demand, frustration, 
 presence, and situational stress. These findings suggest that non-linear p
 atterns 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 devel
 opment of adaptive MR systems that can dynamically adjust task difficulty,
  pacing, or feedback in accordance with the user's cognitive state. Such s
 ystems could enhance user engagement and task performance. This research c
 ontributes to the development of data-driven approaches for real-time cogn
 itive workload assessment and highlights the value of integrating physiolo
 gical monitoring into immersive systems to support adaptive human-machine 
 interaction and personalized training.\n\nTrack: Extended Reality\n\nSessi
 on Chairs: Amelia Warden (University of Michigan) and Emily Fang (North Ca
 rolina State University)\n\n
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