Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Stress Classification of ECG-Derived HRV Features Extracted from Wearable Devices

Version 1 : Received: 24 March 2021 / Approved: 25 March 2021 / Online: 25 March 2021 (16:24:00 CET)

A peer-reviewed article of this Preprint also exists.

Dalmeida, K.M.M.; Masala, G.L.L. HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices. Sensors 2021, 21, 2873. Dalmeida, K.M.M.; Masala, G.L.L. HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices. Sensors 2021, 21, 2873.

Journal reference: Sensors 2021, 21, 2873
DOI: 10.3390/s21082873

Abstract

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence the study will be mainly focusing on the heart rate variability (HRV). This study is aimed to develop a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from HRV measurements obtained from wearable devices. We demonstrate that MLP was the ideal stress classifier by achieving a Recall of 80%. The proposed method can be also used on all applications in which is important to monitor the stress level e. g. in physical rehabilitation, anxiety relief or mental wellbeing.

Keywords

stress; wearable device; machine learning; smart watch; heart rate variability; electrocardiogram

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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