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PPG and EDA dataset collected with Empatica E4 for stress assessment.

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  • 1Department of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, Italy.

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Summary

This study used wearable devices to collect physiological data, like blood pressure and electrodermal activity, from individuals experiencing work-related stressors. The findings enhance understanding of stress in professional settings and aid in developing stress-reduction strategies.

Keywords:
Electrodermal activityElectronic devicesPhotoplethysmographyStress detectionWearable sensors

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Area of Science:

  • Physiological monitoring
  • Stress research
  • Wearable technology

Background:

  • Work and academic environments can induce significant anxiety and distress.
  • Wearable devices enable real-time, personalized monitoring of physiological stress indicators.
  • Understanding the physiological basis of stress is crucial for well-being.

Purpose of the Study:

  • To collect and process physiological data (blood pressure, electrodermal activity) from individuals under induced cognitive, mental, and psychological stressors.
  • To develop a robust data processing pipeline for physiological stress signals.
  • To contribute a substantial dataset for analyzing the relationship between stress and occupational/academic environments.

Main Methods:

  • Utilized the Empatica E4 wearable device to record physiological signals from 29 participants.
  • Implemented a personalized stress-induction protocol simulating work/academic stressors.
  • Developed and applied a data cleaning and processing pipeline for electrodermal activity and blood pressure volume data.

Main Results:

  • Collected a sizable dataset of physiological stress responses.
  • Successfully processed and prepared physiological data for advanced analysis.
  • Established a foundation for understanding individual stress responses in occupational contexts.

Conclusions:

  • The study provides valuable physiological data for comprehending stress in working and academic settings.
  • The developed methods and dataset can support the creation of novel stress-management interventions.
  • Findings can contribute to improving professional performance and mitigating the adverse effects of stress on well-being.