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.

Keywords: adaptive reference ranges; biomedical signal processing; health monitoring; heart rate; non-invasive devices; photoplethysmogram (PPG); respiratory rate; stress-predict dataset.

Figure 1

Study Protocol of the stress-monitoring study including 3 stress-inducing tasks/sessions, 2 self-reporting questionnaire sessions and in-between rest sessions.

Figure 2

Empatica E4 watch.

Figure 3

PPG signal obtained in typical condition, from the green and red light.

Figure 4

Inter-beat-intervals calculation. The green dots show valid peaks while red dots show the discarded peaks.

Figure 5

Pre-processing, signal analysis and post-processing steps of the RR estimation algorithm.

Figure 6

Participants with increased stress levels (a) based on Questionnaire score (b) asked during Interview.

Figure 7

Statistical Analysis of Participant 23: Adaptive referencing range (shaded region) calculated by using approximate EM. (a) Heart rate reading: baseline (green) vs. stress (red) task (b) Respiratory rate: During each baseline (green) vs. stress (red) task.