Task-Centered Design (TCD) of human-system interfaces focuses on supporting the user throughout all phases of tasks, from initiation to completion. TCD typically requires software that monitors aspects of system information to trigger tasks, develop user-friendly information sets, propose task solutions and actions, and confirm actions as directed and approved by the operator. The operator monitors tasks awaiting completion on a Task Manager display. We demonstrate that moment-to-moment operator workload monitoring is greatly facilitated by TCD. Workload estimates were obtained every 2-min over the course of a 35-min test session during an air defense command and control scenario. Workload was readily modeled by the task loading, and the density of track icons on the display. A second study related the unitary workload estimates to NASA TLX workload subscales. Unpublished data from our laboratory indicated that eye activity measures (e.g., blink frequency and duration, pupil diameter, fixation frequency and dwell time) did not improve the estimation of workload. These findings indicate that at least for well-executed TCD systems, eye tracking technologies may be best employed to monitor for fatigue and incongruities between the focus of attention and task requirements. Recent findings using EEG hold promise for the identification of specific brain signatures of confusion, orientation, and loss of situational awareness. Thus the critical element of human directed systems is good initial design. Understanding of the task will lead to system automation that can balance the workload of the operator, who is functioning in a normal state. However, physiological monitoring will be most useful if operators veer beyond their normal conditions and are confused, overloaded, disoriented or have other impairments to their abilities. By detecting the operator's loss of function early, inappropriate operator inputs can potentially be avoided.
[1]
Daniel L Schacter,et al.
Not all false memories are created equal: the neural basis of false recognition.
,
2005,
Cerebral cortex.
[2]
David Kellmeyer,et al.
A Human-Computer Interface Vision for Naval Transformation
,
2003
.
[3]
Daniel L. Schacter,et al.
Neuroanatomical Correlates of Veridical and Illusory Recognition Memory: Evidence from Positron Emission Tomography
,
1996,
Neuron.
[4]
Glenn A. Osga,et al.
Key user support technologies to optimize future command crew efficiency
,
2000,
SPIE Optics + Photonics.
[5]
T. Jung,et al.
Combined eye activity measures accurately estimate changes in sustained visual task performance
,
2000,
Biological Psychology.
[6]
T. Sejnowski,et al.
Awareness during drowsiness: dynamics and electrophysiological correlates.
,
2000,
Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.
[7]
Glenn F. Wilson,et al.
Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks
,
2003,
Hum. Factors.
[8]
T. Jung,et al.
Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness.
,
1996,
Brain research. Cognitive brain research.