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

Mohammod Abdul Motin, Md Santo Ali, Sapnil Sarker Bipro, Meftahul Jannat Nuha, Abid Hasan, Sumaiya Kabir

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Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Mohammod Abdul MotinDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Md Santo AliDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Sapnil Sarker BiproDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Meftahul Jannat NuhaDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Abid HasanDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Sumaiya KabirDepartment of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stress is a natural physiological response that often adversely affects physical and mental health. Wearable devices that offer real-time acquisition of physiological signals provide opportunities for personalised monitoring and effective management of stress. We present, CATSA, a multimodal dataset comprising physiological signals, including blood volume pulse, tri-axis acceleration, electrodermal activity, and average heart rate, collected from 50 participants for monitoring different types of artificially induced stresses from wearable devices. A structured protocol was established to induce cognitive stress in participants, during which physiological signals were recorded using the wrist-worn Empatica E4 device. This dataset can potentially facilitate the development of advanced signal processing and artificial intelligence-based techniques for automated stress monitoring and contribute to the creation of intelligent systems for stress management.

Indexed as

Accelerometer signalBlood volume pulse signalElectrodermal activityPhotoplethysmogram sensorStress monitoringWearable device

Identifiers

PMID41377190

DOI10.1016/j.dib.2025.112295

PMCPMC12686876

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