Remotely measured cognitive stress showed significantly higher normalized low frequency HRV and breathing rates compared to rest, with 85% model prediction accuracy.
Can a novel five band digital camera remotely detect physiological changes (HRV, breathing rate) to accurately predict cognitive stress in participants?
A novel digital camera setup can remotely detect physiological changes like HRV and breathing rate to accurately classify cognitive stress without physical contact.
Remote detection of cognitive load has many powerful applications, such as measuring stress in the workplace. Cognitive tasks have an impact on breathing and heart rate variability (HRV). We show that changes in physiological parameters during cognitive stress can be captured remotely (at a distance of 3m) using a digital camera. A study (n=10) was conducted with participants at rest and under cognitive stress. A novel five band digital camera was used to capture videos of the face of the participant. Significantly higher normalized low frequency HRV components and breathing rates were measured in the stress condition when compared to the rest condition. Heart rates were not significantly different between the two conditions. We built a person-independent classifier to predict cognitive stress based on the remotely detected physiological parameters (heart rate, breathing rate and heart rate variability). The accuracy of the model was 85% (35% greater than chance).
McDuff et al. (2014) studied this question. Remotely measured cognitive stress showed significantly higher normalized low frequency HRV and breathing rates compared to rest, with 85% model prediction accuracy.
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