For future interplanetary space missions, there is a need for an autonomous system that prevents astronauts from potentially hazardous situations. One way in which such a system can help the astronauts, is by predicting their performance. This thesis presents the construction of such a system based on Cognitive task load (CTL) and Emotional state (ES) using Bayesian networks. The network is trained and tested with four different datasets. The first two datasets concern CTL and performance of naval operators working with an adaptive user interface in a lab-setting and naval operators working on a high-tech sailing ship. Especially the ship dataset generalizes very good. The third dataset concerns the ES and performance of participants that are playing a multiplayer first-person shooter game. The last dataset concerns CTL, ES and performance of participants during a three user learning task. The best networks provided respectively a performance estimation of 84.8%, 74.2%, 60.8% and 67.0% while the chance level was 50%. These results indicate that this approach is prommising. Further research is needed to increase the performance such that the method can be used during training and real missions.

1. Performance prediction: preliminary research for manned missions to Mars Stefan kennedie [kennedie[at]gmail.com]

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