HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

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 heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by 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 heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing.

An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress

“…There are several machine learning approaches that have explored physiologically derived markers in general for stress recognition (MacLaughlin et al, 2011;Dhama et al, 2019;Šalkevicius et al, 2019). From feature-based machine learning paradigms which classify various features extracted from wearable sensors, i. e., sleep quality, and percentage of screen time (Sano and Picard, 2013), or heart rate variability (HRV) (Dalmeida and Masala, 2021), and thermal-video recognition of the Initial Systolic Time Interval (Kumar S. et al, 2021), applying the state-of-the-art StressNet. StressNet consists of a Long Short-Term Memory (LSTM)-based architecture to harness spatial-temporal aspects of a continuous signal.…”

COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study

“…Feature selection showed that time domain HRV metrics such as RMSSD and AVNN were important features in stress classification. 39 In our study, tree-based extra-tree classifier had highest sensitivity of 84% and an AUC Score of 84% and an accuracy of 77 %. Feature ranking in our study showed that both demographic features such as mental well-being, higher marriage age, joint family type and HRV features including pNNi20, SDNN and RMSSD were the top classifiers that distinguished between subjects reporting burnout/stress and heathy ones.…”

“… 21 These ECG-derived HRV features as markers for stress detection have been previously used in ML algorithms such as K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). 39 In a study aimed to detect stress based on HRV features derived from Apple watch, MLP was the best ML model with 75% AUC, 80% Recall and 72% F1 score. Feature selection showed that time domain HRV metrics such as RMSSD and AVNN were important features in stress classification.…”

Special Issue “Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures”

“…The authors of [5] presented a study on the physiological parameters (with particular focus on the heart rate variability (HRV)) that can be extracted from wearable devices to detect stress levels in car drivers. The authors developed a predictive model based on different machine learning (ML) methodologies such as K-Nearest Neighbor (KNN), Random Forest (RF), among others that is able to classify the stress level extracted from ECG-derived HRV features [5]. The techniques proposed by the authors show that the HRV features can act as markers for stress level detection, achieving a recall of 80% with the ML models proposed [5].…”

“…The authors developed a predictive model based on different machine learning (ML) methodologies such as K-Nearest Neighbor (KNN), Random Forest (RF), among others that is able to classify the stress level extracted from ECG-derived HRV features [5]. The techniques proposed by the authors show that the HRV features can act as markers for stress level detection, achieving a recall of 80% with the ML models proposed [5].…”