On-Orbit Operations Simulator for Workload Measurement during Telerobotic Training
This work addresses the problem of objectively measuring workload during telerobotic training for space operations, which is incremental as it applies existing methods to a new domain with specific data collection.
The paper developed a telerobotic training simulator for the Canadarm2 on the ISS to measure workload by adding confounding factors like latency and time pressure, and found that simulator performance measures could predict these factors while EEG data showed promising classification for workload assessment.
Training for telerobotic systems often makes heavy use of simulated platforms, which ensure safe operation during the learning process. Outer space is one domain in which such a simulated training platform would be useful, as On-Orbit Operations (O3) can be costly, inefficient, or even dangerous if not performed properly. In this paper, we present a new telerobotic training simulator for the Canadarm2 on the International Space Station (ISS), which is able to modulate workload through the addition of confounding factors such as latency, obstacles, and time pressure. In addition, multimodal physiological data is collected from subjects as they perform a task from the simulator under these different conditions. As most current workload measures are subjective, we analyse objective measures from the simulator and EEG data that can provide a reliable measure. ANOVA of task data revealed which simulator-based performance measures could predict the presence of latency and time pressure. Furthermore, EEG classification using a Riemannian classifier and Leave-One-Subject-Out cross-validation showed promising classification performance and allowed for comparison of different channel configurations and preprocessing methods. Additionally, Riemannian distance and beta power of EEG data were investigated as potential cross-trial and continuous workload measures.