Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors

Zamkah, A., Hui, T., Andrews, S. ORCID: https://orcid.org/0000-0003-4295-2686, Dey, N., Shi, F. and Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 (2020) Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors. Biosensors, 10 (4). 40. ISSN 2079-6374 doi: 10.3390/bios10040040

Abstract/Summary

Skin conductivity (i.e., sweat) forms the basis of many physiology-based emotion and stress detection systems. However, such systems typically do not detect the biomarkers present in sweat, and thus do not take advantage of the biological information in the sweat. Likewise, such systems do not detect the volatile organic components (VOC’s) created under stressful conditions. This work presents a review into the current status of human emotional stress biomarkers and proposes the major potential biomarkers for future wearable sensors in affective systems. Emotional stress has been classified as a major contributor in several social problems, related to crime, health, the economy, and indeed quality of life. While blood cortisol tests, electroencephalography and physiological parameter methods are the gold standards for measuring stress; however, they are typically invasive or inconvenient and not suitable for wearable real-time stress monitoring. Alternatively, cortisol in biofluids and VOCs emitted from the skin appear to be practical and useful markers for sensors to detect emotional stress events. This work has identified antistress hormones and cortisol metabolites as the primary stress biomarkers that can be used in future sensors for wearable affective systems.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/90163
Item Type	Article
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	MDPI
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