Pilot Stress Detection Through Physiological Signals Using a Transformer-Based Deep Learning Model

Abstract

Pilot stress detection is a challenging task and it plays a vital role in improving flight performance and avoiding catastrophic accidents. Many deep learning models have been adopted for stress recognition. However, these models tend to ignore the dependencies between multimodal physiological signals, which can boost the model performance potentially. A transformer-based deep learning framework, which can obtain the position information of multimodal signals by combining a transformer network with a traditional convolutional neural network (CNN), is proposed for detecting pilot stress. The 14 pilots’ physiological data, including electrocardiography (ECG), electromyography (EMG), electrodermal (EDA), respiration (RESP), and skin temperature (SKT), under different stress states are collected for training and validation, and evaluated among different state-of-the-art models. The results show that the proposed model achieves an accuracy of 93.28%, 88.75%, and 84.85% for two-, three-, and four-class classification tasks, respectively, showing faster integration and promising performance.

Nooshin Taheri Chatrudi

Graduate Teaching Assistant

I am a Ph.D. student at the College of Health Solutions, Arizona State University (ASU). Currently, I am working under the supervision of Dr. Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). My research interests include machine learning, clinical informatics, and health monitoring system development.