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.
Highlights
Many operators carry out their activity in complex, high-risk situations and with strong time pressure
To have an accurate and dynamic indicator of task difficulty improving the Operator Functional State (OFS), we considered the following variables to characterize the dynamic task difficulty within SUSIE simulator: “N1” as the number of targets visible on the screen that must be processed, “N2” as the number of messages visible on the screen that must be processed, and “Entropy” as the spatial entropy of the distribution of targets displayed on the screen (by dividing the screen in 8 equal zones, and calculating Pi∗Log(Pi), with Pi the proportion of the targets included in each zones on the total number of targets displayed on the screen)
Since there was a problem of acquisition with cardiac or ocular sensors for 5 participants, only the data of 17 participants were retained to achieve the supervised learning of physiological states
Summary
Many operators carry out their activity in complex, high-risk situations and with strong time pressure. This is the case in air domain, for fighter pilots (Veltman and Gaillard, 1996; Lassalle et al, 2017) or for drone operators (Pomranky and Wojciechowski, 2007; Kostenko et al, 2016). Improving safety and performance of risky missions carried out by these operators becomes an important challenge This one could be solved by adjusting in real time the dialogue and the cooperation between man and machine according to the state of the human operator (Dixon et al, 2005; Wickens et al, 2005; Kostenko et al, 2016). It becomes crucial to assess online the operator’s ability to keep performing the mission, to anticipate
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