Prediction of Perceived Workload From Task Performance and Heart Rate Measures

Abstract

To improve operator efficiency and effectiveness, designers increasingly apply automation to allocate tasks once performed by human operators to the system. Unfortunately, these systems are often complex, potentially imposing increased mental task load on the operator, or placing the operator in a supervisory role where they can become overly dependent on automation. A proposed solution is adaptive automation, which increases automation when an operator is overloaded, and disabled as the operator has spare mental capacity. Changes in performance and physiological measures have shown promise in triggering changes in automation levels. However, the literature lacks well-documented or consistently supported measures for mental workload prediction. The present work sought to define a model which could predict perceived workload as a function of performance and heart rate measures by imposing various levels of task loading on a group of individuals while monitoring their performance, recording their heart rate information with an electrocardiogram and obtaining subjective estimates of mental workload. Heart rate (HR) and several heart rate variability (HRV) measurements where significantly affected by Task Load. This paper describes a linear regression model for predicting participants’ perceived workload as a function of a proposed summary performance metric and HR measures.