Non-Invasive Physiological Signal Analysis for Stress and Fatigue Detection in Wearable Technology
DOI:
https://doi.org/10.32628/IJSRST251222607Keywords:
Stress, Fatigue, Wearable technology, ANOVA, Statistical Analysis, HeatmapAbstract
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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R. Dai, T. Kannampallil, S. Kim, V. Thornton, L. J. Bierut, and C. Lu, “Detecting Mental Disorders with Wearables: A Large Cohort Study,” Apr. 26, 2023. doi: 10.1145/3576842.3582389.
M. Kang and K. Chai, “Wearable Sensing Systems for Monitoring Mental Health,” Sensors, vol. 22, no. 3. Multidisciplinary Digital Publishing Institute, p. 994, Jan. 27, 2022. doi: 10.3390/s22030994.
H. Kim et al., “Recent Advances in Multiplexed Wearable Sensor Platforms for Real-Time Monitoring Lifetime Stress: A Review,” Biosensors, vol. 13, no. 4. Multidisciplinary Digital Publishing Institute, p. 470, Apr. 11, 2023. doi: 10.3390/bios13040470.
D. R. Seshadri et al., “Wearable sensors for monitoring the internal and external workload of the athlete,” npj Digital Medicine, vol. 2, no. 1. Nature Portfolio, Jul. 29, 2019. doi: 10.1038/s41746-019-0149-2.
M. Sheikh, M. Qassem, and P. A. Kyriacou, “Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring,” Frontiers in Digital Health, vol. 3. Frontiers Media, Apr. 07, 2021. doi: 10.3389/fdgth.2021.662811.
S. Gupta, V. Aggarwal. MS Patterh, L. Singh, “Analysis of Foetal Phonocardiography and Clinical Outcomes: A Data-Driven Analysis Using IISc Database”, International Research Journal of Modernization in Engineering Technology and Science, Volume:07, Issue:03, March-2025, pp-7864-7867. doi.10.56726/IRJMETS70309.
V. Aggarwal, S. Gupta, MS Patterh, L. Singh, “Tunable Foetal ECG Compression Using Dingo Optimization Algorithm And Wavelet Transform,” Shu Ju Cai Ji Yu Chu Li/Journal of Data Acquisition and Processing, vol 38, pp 821-827, 2023, doi:10.5281/zenodo.777823.
Y. Rykov, T. Thach, I. Bojić, G. I. Christopoulos, and J. Car, “Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling,” Jul. 15, 2021, JMIR Publications. doi: 10.2196/24872.
A. Muaremi, B. Arnrich, and G. Tröster, “Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep,” May 07, 2013, Springer Science+Business Media. doi: 10.1007/s12668-013-0089-2.
Z. Zainudin, S. Hasan, S. M. Shamsuddin, and S. Argawal, “Stress Detection using Machine Learning and Deep Learning,” Journal of Physics Conference Series, vol. 1997, no. 1, p. 12019, Aug. 2021, doi: 10.1088/1742-6596/1997/1/012019.
Y. S. Can, N. Chalabianloo, D. Ekiz, J. Fernández‐Álvarez, G. Riva, and C. Ersoy, “Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches,” Jan. 01, 2020, Institute of Electrical and Electronics Engineers. doi: 10.1109/access.2020.2975351.
T. Fischer and R. Riedl, “Lifelogging for Organizational Stress Measurement: Theory and Applications,” in SpringerBriefs in information systems, Springer International Publishing, 2018, p. 1. doi: 10.1007/978-3-319-98711-8_1.
Z. Chen et al., “Assessing affective experience of in-situ environmental walk via wearable biosensors for evidence-based design,” Oct. 05, 2018, Elsevier BV. doi: 10.1016/j.cogsys.2018.09.003.
C. Samson and A. Koh, “Stress Monitoring and Recent Advancements in Wearable Biosensors,” Frontiers in Bioengineering and Biotechnology, vol. 8. Frontiers Media, Sep. 02, 2020. doi: 10.3389/fbioe.2020.01037.
V. Prokofieva, С. Костромина, S. Polevaia, and F. Fenouillet, “Understanding Emotion-Related Processes in Classroom Activities Through Functional Measurements,” Oct. 23, 2019, Frontiers Media. doi: 10.3389/fpsyg.2019.02263.
A. Goshvarpour and A. Abbasi, “Affective Visual Stimuli: Characterization of the Picture Sequences Impacts by Means of Nonlinear Approaches.,” Oct. 01, 2015, National Institutes of Health. Accessed: Feb. 2025. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/26649159
R. M. Torrente‐Rodríguez et al., “Investigation of Cortisol Dynamics in Human Sweat Using a Graphene-Based Wireless mHealth System,” Matter, vol. 2, no. 4, p. 921, Feb. 2020, doi: 10.1016/j.matt.2020.01.021.
B. A. Hickey et al., “Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review,” Sensors, vol. 21, no. 10. Multidisciplinary Digital Publishing Institute, p. 3461, May 16, 2021. doi: 10.3390/s21103461.
V. Aggarwal, S. Gupta, MS Patterh, S. Bansal, “Meta-Analysis approach to assess ECG compression methods: A systematic Review,” 14th ICCCNT, IEEE Conference July 6-8, 2023 IIT – Delhi.
V. Aggarwal, S. Gupta, MS Patterh, L. Singh, “Meta-Analysis Method To Evaluate EMG Compression Techniques: A Systematic Review,” Vol. 38(3), pp 6746-6759, 2023, doi: 10.5281/zenodo.7778381.
https://physionet.org/content/wearable-device-dataset/1.0.0/
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