Influences of Mental Stress Level on Individual Identification using Wearable Biosensors

As wearable biosensors become increasingly popular, personalized services are applied in various scenarios to identify individuals based on biometric signals with unique inherent characteristics of each individual. However, the varying levels of mental stress in different scenarios (e.g., work and relaxation) in individuals would obscure whether such stress could influence the identification process. To address this, we built CNN-based models to identify 12 subjects under four levels of mental stress by using three common biometric signals: R-R interval, galvanic skin reaction, and respiration. Our analysis revealed that individuals can be more easily identified when they are at certain levels of mental stress. The examination of the effect of mental stress on the identification of different subjects found that the inherent rhythm differences in biometric signals among individuals could affect the accuracy of identification. We further validated how stress levels impact individual identification over various time periods.