Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information
🤖 AI Summary
Real-time assessment of operator mental states and cognitive load remains challenging in human-swarm robotic collaboration.
Method: This paper introduces subjective time perception (i.e., “fast”/“slow” judgment) as a novel, dynamic physiological proxy, estimated non-invasively and with low latency from eye-tracking data. We integrate SVM/LSTM classifiers, subjectively validated questionnaire calibration, and an individualized self-supervised pretraining framework—requiring only 30 seconds of per-user calibration for cross-subject generalization.
Contribution/Results: To our knowledge, this is the first work to formalize subjective time perception as a quantifiable cognitive state surrogate, overcoming the reliance of conventional load metrics on prolonged physiological recordings. Experiments in multi-robot control tasks demonstrate statistically significant improvements in binary classification accuracy over baselines, confirming the high discriminative power of oculomotor signals for cognitive stress states. The approach establishes a new paradigm for adaptive human–machine intervention in collaborative systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Assessing operator mental state using time perception
Classifying time perception via eye-tracking in robot control
Adapting swarm systems based on human cognitive strain
Innovation
Methods, ideas, or system contributions that make the work stand out.
Eye-tracking data classifies subjective time perception
Individual-based pretraining with 30 seconds data
Closed-loop control using physiological data classification
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