Toward Real-time Assessment of Workload: A Bayesian Inference Approach. (November 2019) - Luo, Ruikun; Wang, Yifan; Weng, Yifan; Paul, Victor; Brudnak, Mark J.; Jayakumar, Paramsothy; Reed, Matt; Stein, Jeffrey L.; Ersal, Tulga; Yang, X. Jessie
Workload management is of critical concern in teleoperation of unmanned vehicles, because high workload can lead to sub-optimal task performance and can harm human operators' long-term well-being. In the present study, we conducted a human-in-the-loop experiment, where the human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) and performed a secondary visual search task. We measured participants' gaze trajectory and pupil size, based on which their workload level was estimated. We proposed and tested a Bayesian inference (BI) model for assessing workload in real time. Results show that the BI model can achieve an encouraging 0.69 F 1 score, 0.70 precision, and 0.69 recall.