Human Supervisor Workload Prediction: Lag Horizon Selection
For teleoperation systems, this work provides insights into selecting lag horizons for workload prediction, enabling more efficient models with longer prediction horizons.
This paper investigates physiological sensor-driven human workload prediction, focusing on the impact of lag horizons on forecasting models. It finds that univariate predictions require 240-second lag horizons, while multivariate predictions suffice with 120-second horizons, enabling longer prediction horizons.
Teleoperation systems must be aware of the human's workload during missions to maintain operator performance. Prior work employed wearable physiological sensor response metrics to estimate current human workload; however, these estimates only enable robots to respond to under- or overload conditions reactively. Current human workload prediction approaches are limited to very short prediction horizons and fail to investigate variable lag horizons' impact on those predictions. This manuscript investigates physiological sensor driven human workload prediction focusing on the impact of lag horizons on both univariate and multivariate time series forecasting models, with longer prediction horizons than the workload prediction state-of-the-art (i.e., > 30 seconds using Long Short-Term Memory networks). Models were trained using data from a 64 participant non-sedentary supervisory environment NASA Multi-Attribute Task Battery-II human subjects evaluation. A key finding is that univariate workload predictions required 240 second lag horizons, whereas multivariate workload predictions sufficed with 120 second lag horizons. This finding indicates additional workload components reduce lag horizon requirements, enabling more efficient models with longer prediction horizons.