Abstract
When operators are overwhelmed, judicious employment of automation can help. Ideally, an adaptive system which can accurately estimate current operator workload can more effectively employ automation. Supervised machine learning models can be trained to estimate workload from operator-state parameters sensed by on-body sensors which, for example, collect heart rate or brain activity information. Unfortunately, estimating operator workload using trained models is limited: using a model trained in one context can yield poor estimation of workload in another. This research examines the efficacy of using two regression-tree alternatives (random forests and pruned regression trees) to decrease workload estimation cross-application error. The study is conducted for a remotely piloted aircraft simulation under two context-switch scenarios 1) across human operators and 2) across task conditions. While cross-task results were inconclusive, both algorithms significantly reduced cross-application error in estimating workload across operators, and random forests performed best in cross-operator applicability.