Real-Time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach

Ruikun Luo

doi:10.1080/10447318.2023.2205274

Real-Time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach

2023-05-04

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DOI：10.1080/10447318.2023.2205274

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Workload management is a critical concern in shared control of unmanned ground vehicles. In response to this challenge, prior studies have developed methods to estimate human operators' workload by analyzing their physiological data. However, these studies have primarily adopted a single-model-single-feature or a single-model-multiple-feature approach. The present study proposes a Bayesian inference model to estimate workload, which leverages different machine learning models for different features. We conducted a human subject experiment with 24 participants, in which a human operator teleoperated a simulated High Mobility Multipurpose Wheeled Vehicle (HMMWV) with the help from an autonomy while performing a surveillance task simultaneously. Participants' eye-related features, including gaze trajectory and pupil size change, were used as the physiological input to the proposed Bayesian inference model. Results show that the Bayesian inference model achieves a 0.823 F-1 score, 0.824 precision, and 0.821 recall, outperforming the single models.

Keywords:

Human-automation interaction

human-autonomy interaction

Bayesian inference

workload estimation

Real-Time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach

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