Supervised Classification of Operator Functional State Based on Physiological Data: Application to Drones Swarm Piloting

Abstract

To improve the safety and the performance of operators involved in risky and demanding missions (like drone operators), human-machine cooperation should be dynamically adapted, in terms of dialogue or function allocation. To support this reconfigurable cooperation, a crucial point is to assess online the operator’s ability to keep performing the mission. The article explores the concept of Operator Functional State (OFS), then it proposes to operationalize this concept (combining context and physiological indicators) on the specific activity of drone swarm monitoring, carried out by 22 participants on simulator SUSIE. With the aid of supervised learning methods (Support Vector Machine, k-Nearest Neighbors, and Random Forest), physiological and contextual are classified into three classes, corresponding to different levels of OFS. This classification would help for adapting the countermeasures to the situation faced by operators.