A Real-Time Closed-Loop System for Predicting and Counteracting Lapses of Attention
Abstract
Vigilance tasks, from driving to surveillance to security remain important and frequent tasks for the U.S. Army. Yet the difficulty users have sustaining vigilance is well known. Augmented cognition offers new methods for supporting sustained vigilance via a closed-loop attention management system (CLAM). A CLAM system monitors operators' psychophysiology for signs of inattention and then triggers a countermeasure to rouse them and help them sustain vigilance and good task performance. There are many requirements for bringing this concept to fruition, including minimal or no contact psychophysiological measures that are minimally invasive or constraining, accurate and precise prediction of attention level and task performance, and effective interface modifications. Over the course of this 3-year project, the authors have investigated the following: (1) effective combinations of psychophysiological measures of inattention, (2) effective countermeasures to rouse and re-engage participants once inattention is detected, and (3) a complete closed-loop system for monitoring and sustaining attention and task performance. The project has produced three book chapters and one conference paper. A journal article is under review. This report includes abstracts describing the work during each year of the project.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 07, 2008
- Accession Number
- ADA499757
Entities
People
- David A. Kobus
- Mark St. John
- Matthew R. Risser