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A multi-modal wearable dataset for cognitive attention and task-based stress analysis.

Mohammod Abdul Motin1, Md Santo Ali1, Sapnil Sarker Bipro1

  • 1Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Data in Brief
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Summary

This study introduces the CATSA dataset for stress monitoring using wearable devices. It aids in developing AI for personalized stress management by analyzing physiological signals.

Keywords:
Accelerometer signalBlood volume pulse signalElectrodermal activityPhotoplethysmogram sensorStress monitoringWearable device

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Area of Science:

  • Physiology
  • Biomedical Engineering
  • Data Science

Background:

  • Stress significantly impacts physical and mental health.
  • Wearable devices enable real-time physiological monitoring for stress management.
  • Personalized stress monitoring requires comprehensive physiological datasets.

Purpose of the Study:

  • To introduce the CATSA multimodal dataset for stress monitoring.
  • To facilitate the development of AI-based stress detection and management systems.
  • To provide a resource for research on wearable sensor data analysis.

Main Methods:

  • Collected multimodal physiological signals (blood volume pulse, electrodermal activity, acceleration, heart rate) from 50 participants.
  • Utilized the Empatica E4 wrist-worn device for data acquisition.
  • Employed a structured protocol to induce cognitive stress in participants.

Main Results:

  • The CATSA dataset comprises diverse physiological signals under induced stress conditions.
  • Data includes blood volume pulse, tri-axis acceleration, electrodermal activity, and average heart rate.
  • The dataset is suitable for developing and validating stress monitoring algorithms.

Conclusions:

  • The CATSA dataset is a valuable resource for advancing automated stress monitoring.
  • It supports the creation of intelligent systems for personalized stress management.
  • Facilitates research in signal processing and AI for physiological data analysis.