Presentation
70. Using Neural Data to Classify Workload: A Refresh of the SynWin Task Battery
SessionPoster Session 1
DescriptionHarnessing neural data collected via functional near-infrared spectroscopy (fNIRS) can be a game-changing tool for assessing individual and team states. However, most neural measurement devices are plagued by barriers like cost, portability, and ease of use by non-experts. Therefore, to aid future research in online state monitoring, we aimed to develop a lightweight fNIRS device and to refresh a previously validated multitask battery. The fNIRS system was tested alongside an updated and contextualized version of the synthetic work environment (SynWin) task battery (Elsmore, 1994) that we call Aviator SynWin. Aviator SynWin, like its predecessor, requires participants to simultaneously perform four unrelated tasks and allows researchers to adjust the timing of concurrent task events to induce distinct levels of task difficulty. We trained preliminary individualized workload models and found classification accuracy to be low, suggesting that our calibration task did not generalize well to the Aviator SynWin task.
Event Type
Poster
TimeWednesday, September 11th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
Aerospace Systems
Cognitive Engineering & Decision Making
Computer Systems
Forensics Professional
Health Care
Human Performance Modeling
Individual Differences in Performance
Perception and Performance
Product Design
Safety
Training
Usability and System Evaluation
Extended Reality