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Enhancing stress detection through HRV in wearable technology

Shterev, Veselin (2025) Enhancing stress detection through HRV in wearable technology.

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Abstract:Heart rate variability (HRV), a well-known biomarker for stress and an indicator of autonomic nervous system function, is being used more and more in wearables these days to identify stress. However, these devices’ current HRV-based algorithms still have poor accuracy and reliability. This study presents a systematic review of HRV-based stress detection methods, focusing on research mostly published after 2016. A total of 35 articles were reviewed, with 23 studies meeting the inclusion criteria. The review categorizes stress detection approaches based on the integration of HRV with other physiological stress biomarkers (e.g., skin conductance, blood pressure) and the use of multi-modal sensor systems. Key findings reveal that combining HRV with additional biomarkers enhances the precision of stress detection, although challenges remain in achieving consistent results across diverse populations and conditions. The use of multi-sensor approaches, including wearable and non-invasive technologies, has shown promise but requires further validation in real-world settings. This review shows a structured overview of advancements in HRV-based stress detection, including a detailed summary of the settings, validation methods, and performance metrics of the included studies. These findings offer valuable insights for future research and the development of more robust and reliable wearable stress monitoring technologies.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/105108
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