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Wearable Sensor Suite for Early Detection of Cognitive Overload in Military Settings

The US Army Research Office's SBIR Phase II project aims to develop an innovative wearable physiological sensor suite for early detection of cognitive overload. Supported by the Aberdeen Test Center's Remote Neurological Monitoring Program, the initiative explores the statistical relationships among stress, mental workload, and performance. Key factors include engagement levels, task workload, mental fatigue, and robust signal processing methods. By capturing multimodal signals, the goal is to create stable models that accurately predict cognitive states with minimal recalibration, enhancing operational readiness and decision-making in high-demand environments.

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Wearable Sensor Suite for Early Detection of Cognitive Overload in Military Settings

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  1. Sponsored by • US Army Research Office SBIR Phase II: “Wearable Physiological Sensor Suite For Early Detection Of Cognitive Overload” • US Army Aberdeen Test Center: “Remote Neurological Monitoring Program”

  2. Stress, Overload, and Performance

  3. Mental State Estimation Statisitcal Relationships

  4. Definitions • Engagement: selection of a task as the focus of attention and effort • Workload: significant commitment of processing resources to an engaged task • Visual, Auditory, Haptic • Psychomotor • Cognitive (memory, executive) • Overload: task demands outstrip available processing resources • Mental Fatigue: desire to withdraw attention and effort from an engaged task associated with extended performance (~45 min)

  5. Overload Patterns in Multimodal Signals

  6. Multimodal Classifier Testbed

  7. Multimodal Classifier Testbed

  8. Multimodal Classifier Testbed

  9. Multimodal Classifier Testbed

  10. Multimodal Classifier Testbed C2 C1

  11. Engagement/Workload Related EEG Sources

  12. Fatigue-Related EEG Sources Black = Alert Red = Mentally Fatigued Parietal Fz Pz Alpha Frontal Theta

  13. Other Sources

  14. Experimental Controls • Task learning • Time of day and time on task • Test day • Food consumption • Neurotoxic effects • Test environment • Inadequate measurement of physiological variance • Inadequate definition of ground truth workload levels: • Expert analysis and scoring of replayed videos • Logging all user inputs • Measuring reaction times to probes

  15. Validation of Workload Manipulation NASA - TLX questionnaires P300

  16. Within-day Results

  17. Day-to-day Results

  18. Stabilizing Classifiers

  19. Stabilized Day-to-day Results

  20. Summary • Biosignals exhibit high sensitivity to mental states, such as engagement, workload, and fatigue • Accurate biosignal-based models or “gauges” can be developed under controlled conditions and extended to new conditions • However, cognitive gauges are not very stable over time, due to behavioral, strategic, and physiological variability • Multimodal models capture a wide range of behavioral and physiological variability, improving robustness of gauges over time and conditions • Signal processing and computational methods help, but are not enough to yield stable models • Some recalibration or model adaptation is currently required • We seek ways to stabilize models with a minimum of recalibration

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