On-line estimation of human operator workload

Abstract

An algorithm that is able to estimate adaptively the performance of a human operator in a series of overlapping tasks is presented. The algorithm has its foundations in the multiple resource pool model of human operator workload. Each task performed by the operator is split into a number of subtasks, with each subtask in turn modeled by a finite-impulse-response (FIR) filter channel. As each subtask is executed, the channel bandwidths are updated to provide the weights of the FIR filter and estimates of the correlation between the different channels. The correlation gives an indication of the ability to perform tasks simultaneously. The algorithm was tested in a real-time operator framework in which the operator had to perform different simple tasks simultaneously.<>