An Ensemble Classification Model With Unsupervised Representation Learning for Driving Stress Recognition Using Physiological Signals

摘要

This paper presents an ensemble classification model with unsupervised feature learning for driving stress recognition under real-world driving conditions. The driving stress is detected using drivers' different physiological signals, specifically the electromyogram, electrocardiogram, galvanic skin response, heart rate and respiration. The proposed model consists of two modules: 1) a multilayer representation learning module using autoencoder as its building block. The autoencoders are trained with a quasi-automated, non-gradient descent based unsupervised learning algorithm; 2) an ensemble classification module under the AdaBoost framework. The proposed model is completely data driven, does not require additional feature extraction and feature selection process, and can perform in an end-to-end way in which it takes the physiological signal as the input instead of the handcrafted features. Experimental results show that our proposed model can effectively recognize the driving stress with fewer physiological sensors compared with most of state of the art methods. Experiments also demonstrate that the proposed model can simplify the model structure tuning and improve the learning efficiency compared with the baseline deep learning model.