Machine Learning Based Prediction of Future Stress Events in a Driving Scenario

TL;DR

This paper develops a machine learning model that predicts a driver's stress level up to one minute in advance using physiological signals, aiming to enable preemptive stress mitigation in driving scenarios.

Contribution

It introduces a novel predictive model utilizing multi-modal physiological data and a comprehensive feature extraction process for early stress detection in drivers.

Findings

Achieved 94% average accuracy in stress prediction

Effective use of multi-modal physiological signals

Potential application in vehicle stress prevention systems

Abstract

This paper presents a model for predicting a driver's stress level up to one minute in advance. Successfully predicting future stress would allow stress mitigation to begin before the subject becomes stressed, reducing or possibly avoiding the performance penalties of stress. The proposed model takes features extracted from Galvanic Skin Response (GSR) signals on the foot and hand and Respiration and Electrocardiogram (ECG) signals from the chest of the driver. The data used to train the model was retrieved from an existing database and then processed to create statistical and frequency features. A total of 42 features were extracted from the data and then expanded into a total of 252 features by grouping the data and taking six statistical measurements of each group for each feature. A Random Forest Classifier was trained and evaluated using a leave-one-subject-out testing approach. The model achieved 94% average accuracy on the test data. Results indicate that the model performs well and could be used as part of a vehicle stress prevention system.