Multitasking Performance and Workload during a Continuous Monitoring Task

In this paper, we used signal detection theory (SDT) and an eye-head integrated tracking system as a tool to measure the multitasking performance and workload in a continuous monitoring task. By using both eye-gaze/head position data from the tracking system and the performances of all tasks, we developed metrics which can represent the overall performance and workload in a multitasking environment. The primary task was a process monitoring task, which contained total 52 gauges (flow, level, temperature, and pressure). The secondary task was Multi-Attribute Task Battery (MATB), which consisted of system monitoring, target tracking, and dynamic resource management. The results of this study showed the developed metrics could evaluate various levels of multitasking performance and workload in the gauge-monitoring task. In addition, participant’s Situation Awareness accuracy and NASA-TLX were used to validate these metrics under different complexity scenarios.