# Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers

Stefano Betti, Raffaele Molino Lova, Erika Rovini, Giorgia Acerbi, Luca Santarelli, Manuela Cabiati, Silvia Del Ry, Filippo Cavallo

Research output: Contribution to journalArticlepeer-review

## Abstract

Objective: The objectives of this work is to develop and test the ability of a wearable physiological sensors system, based on ECG, EDA and EEG, to capture human stress and to assess whether the detected changes in physiological signals correlate with changes in salivary cortisol level, which is a reliable, objective biomarker of stress. Methods: 15 healthy participants, seven males and six females, mean age 40.8 <formula><tex>$\pm$</tex></formula> 9.5 years, wore a set of three commercial sensors to record physiological signals during the Maastricht Acute Stress Test, an experimental protocol known to elicit robust physical and mental stress in humans. Salivary samples were collected throughout the different phases of the test. Statistical analysis was performed using a Support Vector Machine (SVM) classification algorithm. A correlation analysis between extracted physiological features and salivary cortisol levels was also performed. Results: 15 features extracted from heart rate variability, electrodermal and electroencephalography signals showed a high degree of significance in disentangling stress from a relaxed state. The classification algorithm, based on significant features, provided satisfactory outcomes with 86% accuracy. Furthermore, correlation analysis showed that the observed changes in physiological features were consistent with the trend of salivary cortisol levels (R2 = 0.714). Conclusion: The tested set of wearable sensors was able to successfully capture human stress and quantify stress level. Significance: The results of this pilot study may be useful in designing portable and remote control systems, such as medical devices used to turn on interventions and prevent stress consequences.

Original language English IEEE Transactions on Biomedical Engineering Nov 18 2017 https://doi.org/10.1109/TBME.2017.2764507 Published - Aug 2018

## Keywords

• Cortisol
• Machine learning
• Physiological sensors
• Stress detection
• Work environments

## ASJC Scopus subject areas

• Biomedical Engineering

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