Non-Invasive Physiological Signal Analysis for Stress and Fatigue Detection in Wearable Technology

This study analyses physiological signals collected from wearable devices during structured stress induction and exercise sessions. Data from 94 participants were used to compare physiological responses across gender and activity conditions (aerobic, anaerobic, and stress). The study focused on features such as accelerometer (ACC), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), inter-beat interval (IBI), and skin temperature (TEMP). Results revealed significant physiological differences between genders and activity conditions, with an ANOVA test confirming significant variations in HR across conditions (p < 0.0001). These findings contribute to improving stress and exercise classification models using wearable sensor data.

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Publication Details

Journal
International Journal of Scientific Research in Science and Technology
Published
2025-04-09
DOI
https://doi.org/10.32628/ijsrst251222607
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
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article

Non-Invasive Physiological Signal Analysis for Stress and Fatigue Detection in Wearable Technology

Vibha Aggarwal, Manjeet Singh Patterh, S. V. Gupta, Lovepreet Singh
International Journal of Scientific Research in Science and Technology
Non-Invasive Vital Sign Monitoring
article

Non-Invasive Physiological Signal Analysis for Stress and Fatigue Detection in Wearable Technology

Vibha Aggarwal, Manjeet Singh Patterh, S. V. Gupta, Lovepreet Singh
article en

Abstract

This study analyses physiological signals collected from wearable devices during structured stress induction and exercise sessions. Data from 94 participants were used to compare physiological responses across gender and activity conditions (aerobic, anaerobic, and stress). The study focused on features such as accelerometer (ACC), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), inter-beat interval (IBI), and skin temperature (TEMP). Results revealed significant physiological differences between genders and activity conditions, with an ANOVA test confirming significant variations in HR across conditions (p < 0.0001). These findings contribute to improving stress and exercise classification models using wearable sensor data.

International Journal of Scientific Research in Science and TechnologyVol. 12(2)
University of the Punjab (PK), GNA University (IN), Punjabi University (IN)
Openalex Percentile: Top 93%
Non-Invasive Vital Sign Monitoring
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