用眼动数据实时判断操作员的时间感知，助力人机协同减负
Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information
- 通过眼动数据结合问卷，识别操作员主观时间感知
- 仅需30秒个体数据预训练即可准确估计时间感知
- 适合人机交互、智能机器人系统等场景的实时状态监测
随着自动化与移动机器人的发展，工作环境不断变化，对操作员的生产力期望提高，认知负荷随之上升，可能引发压力与认知过载。准确评估操作员心理状态对维持表现与福祉至关重要。本文利用主观时间感知作为敏感且低延迟的身心状态指标，因其受压力与认知负荷影响而发生改变。时间感知偏差会影响决策、反应速度与任务效率，是自适应人-蜂群交互系统的重要参考。研究以人操控多台小型移动机器人场景为例，采集眼动数据，并基于问卷数据对其进行主观时间感知分类。结果表明，可成功从眼动数据中估计个体的时间感知。该方法在仅使用30秒个体数据进行预训练的情况下表现良好。未来工作将构建闭环控制系统，使机器人能自动解析生理数据并响应操作员需求。
原文摘要 · Abstract (English)
As automation and mobile robotics reshape work environments, rising expectations for productivity increase cognitive demands on human operators, leading to potential stress and cognitive overload. Accurately assessing an operator's mental state is critical for maintaining performance and well-being. We use subjective time perception, which can be altered by stress and cognitive load, as a sensitive, low-latency indicator of well-being and cognitive strain. Distortions in time perception can affect decision-making, reaction times, and overall task effectiveness, making it a valuable metric for adaptive human-swarm interaction systems. We study how human physiological signals can be used to estimate a person's subjective time perception in a human-swarm interaction scenario as example. A human operator needs to guide and control a swarm of small mobile robots. We obtain eye-tracking data that is classified for subjective time perception based on questionnaire data. Our results show that we successfully estimate a person's time perception from eye-tracking data. The approach can profit from individual-based pretraining using only 30 seconds of data. In future work, we aim for robots that respond to human operator needs by automatically classifying physiological data in a closed control loop.
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