通过生理信号预测操作员负荷，发现多变量模型只需120秒滞后期。
Human Supervisor Workload Prediction: Lag Horizon Selection
- 用生理传感器数据建模，研究不同滞后时间对负荷预测的影响。
- 多变量模型仅需120秒滞后即可准确预测，优于现有30秒上限。
- 适合需要提前干预的远程操控系统设计者参考。
遥操作系统需感知操作员在任务中的工作负荷以维持性能。以往研究利用可穿戴生理传感器指标估计当前负荷，但仅能被动响应过载或欠载。现有负荷预测方法普遍局限于极短预测时长，且未考察可变滞后时长的影响。本文聚焦生理信号驱动的负荷预测，探究滞后时长对单变量与多变量时间序列模型的影响，实现超过30秒的预测时长（采用长短期记忆网络）。基于64名非静坐状态下参与者的NASA多属性任务电池-II实验数据训练模型。关键发现：单变量负荷预测需240秒滞后时长，而多变量预测仅需120秒。这表明引入更多负荷维度可降低滞后需求，支持更高效、更长时距的预测模型。
原文摘要 · Abstract (English)
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.
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