Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information
This addresses cognitive overload and stress in human operators of robotic systems, but it is incremental as it applies an existing method to a new domain-specific scenario.
The study tackled the problem of assessing a human operator's mental state in a multi-robot control scenario by using eye-tracking data to classify subjective time perception, achieving successful estimation with individual-based pretraining requiring only 30 seconds of data.
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.