March 29, 2023SensorsOpen Access

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

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Why the study?

Timely identification of stress symptoms is crucial to prevent stress-related complications, necessitating continuous and personalized stress monitoring via wearable devices.

Can machine-learning algorithms analyzing signals from the Empatica E4 bracelet accurately differentiate between stressful and non-stressful situations in human subjects?

Structured PICO

Can machine-learning algorithms analyzing signals from the Empatica E4 bracelet accurately differentiate between stressful and non-stressful situations in human subjects?

Population

29 subjects

Intervention

Machine-learning algorithms (Random Forest, SVM, and Logistic Regression) analyzing photoplethysmographic and electrodermal activity signals from the Empatica E4 bracelet

Outcome

Efficacy in differentiating between stressful and non-stressful situations (accuracy, precision, recall, F1-measure)surrogate

Machine learning algorithms, particularly Random Forest, can detect stress with moderate accuracy using photoplethysmographic and electrodermal activity signals from a wearable bracelet.

Implication

May support wearable stress monitoring; leaves open prospective validation before clinical use.

Abstract

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).