Adaptive Training in an Unmanned Aerial Vehicel: Examination of Several Candidate Real-time Metrics
Abstract
The present study examined the sensitivity of several candidate metrics of real-time workload within the spatial component of an unmanned aerial vehicle (UAV) task. Advanced Brain Monitoring s (ABM) wireless B-Alert system was used to collect participant s EEG workload and engagement data. Eye tracking data was also collected. The UAV simulation required participants to report heading information of moving vehicles, as seen from the UAV. There were four blocks of difficulty, over which a significant performance decrement was shown. Additionally, participants rated their workload significantly higher and pupil diameter significantly increased across blocks of increasing difficulty, as well as within each block during periods of highest mental demand. ABM s workload and engagement metrics however did not show a significant change over or within blocks. The results showed that pupil diameter shows promise as a correlate of mental workload.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2010
- Accession Number
- ADA606301
Entities
People
- Anna Cole
- Carryl Baldwin
- Ciara Sibley
- Daniel Roberts
- Gregory Gibson
- Jane Barrow
- Joseph Coyne
Organizations
- United States Naval Research Laboratory