Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset

Abstract

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

Predicting stress in first-year college students using sleep data from wearable devices

“…Our study adds to the growing literature utilizing sleep and cardiorespiratory markers to identify changes in health [ 36 , 61 , 62 ]. Mechanistically there is a relationship between stress and decreased parasympathetic regulation [ 63 ] which modulates the neural pathways affecting heart rate, heart rate variability, and respiratory rate [ 64 ].…”

Contribution of EEG biosignals for Stress Detection

“…The field of stress detection using physiological signals has gathered significant interest in recent years due to its potential for enhancing mental health monitoring and well-being [53]. In this work, we focus on the study of the role of EEG data for stress detection.…”

Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

“…Like machine learning, this approach heavily depends on the signal selected to identify stress and quality of such signal. For this reason, all the studies utilizing the statistical approach included in the review employ signal filtering methods based on motion detection [21] and filters [41,50]. Regarding the signals examined in the studies, two focused on Electrodermal Activity (EDA) [21,50], while one considered Photoplethysmogram (PPG) [41].…”

“…For this reason, all the studies utilizing the statistical approach included in the review employ signal filtering methods based on motion detection [21] and filters [41,50]. Regarding the signals examined in the studies, two focused on Electrodermal Activity (EDA) [21,50], while one considered Photoplethysmogram (PPG) [41]. In general, the pipeline employed by studies based on statistical approaches can be summarized in two main steps, with an optional third step: collecting a baseline (composed of filtered biosignals) for the subject, constructing rules through statistical algorithms, and potentially adapting these rules on the fly over time.…”