Remote measurement of cognitive stress via heart rate variability

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).

[1]  Peter Robinson,et al.  Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  G. Lu,et al.  A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects , 2009, Journal of medical engineering & technology.

[3]  D. Eckberg,et al.  Important influence of respiration on human R-R interval power spectra is largely ignored. , 1993, Journal of applied physiology.

[4]  L. O. Svaasand,et al.  Remote plethysmographic imaging using ambient light. , 2008, Optics express.

[5]  A. Otsuka,et al.  Spectral change in heart rate variability in response to mental arithmetic before and after the beta-adrenoceptor blocker, carteolol , 2005, Clinical Autonomic Research.

[6]  E Kristal-Boneh,et al.  Heart rate variability in health and disease. , 1995, Scandinavian journal of work, environment & health.

[7]  Rosalind W. Picard,et al.  Non-contact, automated cardiac pulse measurements using video imaging and blind source separation , 2022 .

[8]  Frédo Durand,et al.  Detecting Pulse from Head Motions in Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[10]  Dvijesh Shastri,et al.  Perinasal Imaging of Physiological Stress and Its Affective Potential , 2012, IEEE Transactions on Affective Computing.

[11]  Daniel McDuff,et al.  Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera , 2014, IEEE Transactions on Biomedical Engineering.

[12]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[13]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.

[14]  L. Luecken,et al.  Measuring Task-related Changes in Heart Rate Variability , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Daniel McDuff,et al.  Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam , 2011, IEEE Transactions on Biomedical Engineering.

[16]  Maja Pantic,et al.  Local Evidence Aggregation for Regression-Based Facial Point Detection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.