Stress Detection Based on Fusion of Multimodal Physiological signals using Dempster-Shafer Evidence Theory

Subject Areas : Renewable energy

Sara Majlesi ¹ (Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Mahdi Khezri ² (Digital Processing and Machine Vision Research Center- Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2021-06-18 Accepted : 2021-08-20 Published : 2021-12-22

Keywords: SVM Classifier, Stress detection, physiological signals, Dempster-Shafer fusion method,

Abstract :

Detecting and controlling stress levels in drivers is especially important to reduce the potential risks while driving. Accordingly, in this study, a detection system was presented to identify four levels of stress (low, neutral, high and very high) in drivers based on physiological signals. The proposed method used the drivedb database, which includes the recording of physiological signals from 17 healthy volunteers while driving on specific routes on city streets and highways. A set of statistical and entropy features along with morphological features that were calculated only for the ECG signals, were used. The calculated features were applied as inputs to the classification units to detect stress levels. Support vector machine (SVM), k nearest neighbors (kNN) and decision tree (DT) were evaluated as classification methods. The main purpose of this study was to improve the accuracy of stress level detectionusing the idea of classifiers fusion. To achieve this goal, the combination of individual classification units, each of which used only the features of one of the ECG, EMG and GSR signals, was performed by the Demster-Shafer method. Using genetic algorithm as feature selection method, SVM classifier and Dempster-Shafer fusion strategy, the best stress detection accuracy of 96.9% was obtained. While the highest detection accuracy among individual classifiers was 75% and obtained by a subsystem that used ECG features.The results show significant performance of the proposed method compared to previous studies that used the same dataset.

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