Symmetry, Vol. 17, Pages 1298: Intelligent Detection of Cognitive Stress in Subway Train Operators Using Multimodal Electrophysiological and Behavioral Signals

Symmetry doi: 10.3390/sym17081298

Authors:
Xinyi Yang
Lu Yu

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R2) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies.

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