System and method for camera-based stress determination

1. A method for camera-based stress determination of a human individual, the method comprising:

receiving an image sequence capturing a body part of the human individual;

determining a plurality of regions-of-interest (ROIs) of the body part based on features of the body part;

determining a set of bitplanes in the captured image sequence for each ROI that represent HC changes of the human individual using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets;

determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes;

for each ROI, determining intervals between heartbeats based on peaks in the HC change signal;

determining heart rate variability using the intervals between heartbeats;

determining a stress level using at least one determination of a standard deviation of the heart rate variability; and

outputting the stress level.

2. The method of claim 1 , wherein the cardiovascular data comprises hemodynamic changes measured by an electrocardiograph.

3. The method of claim 1 , wherein the bitplanes are in a red channel, green channel, and blue channel of each image of the image sequence.

4. The method of claim 1 , wherein the machine learning model comprises a Long Short Term Memory (LSTM) neural network.

5. The method of claim 4 , wherein the output of the LSTM neural network comprises a matrix of bitplane composition weights as the determined set of bitplanes.

6. The method of claim 1 , wherein the body part is the individual's face.

7. The method of claim 6 , wherein determining a plurality of ROIs comprises tracking the human individual's face in each frame of the captured image sequence to track the ROIs.

8. The method of claim 1 , wherein determining intervals between heartbeats comprises:

applying fast Fourier transform (FFT) and band pass filtering to determine a principle frequency component;

using the principle frequency component, reconstructing peaks of each heartbeat; and

determining intervals between the reconstructed peaks.

9. The method of claim 1 , wherein determining heart rate variability comprises generating a Poincaré plot of the heartbeat intervals.

10. The method of claim 9 , wherein determining the stress level comprises:

determining a first standard deviation of points of heart rate variability in a direction perpendicular to a line of identity of the Poincare plot;

determining a first standard deviation of points of heart rate variability in a direction that is along the line of identity;

determining a measure of stress as a correlation to the second standard divided by the first standard deviation.

11. The method of claim 10 , wherein determining the measure of stress further comprises performing a Fisher z-transformation to the second standard divided by the first standard deviation.

12. A system for camera-based stress determination of a human individual, the system comprising at least one processing unit and a data storage, the at least one processing unit in communication with the data storage and configured to execute:

a transdermal optical imaging (TOI) module to receive an image sequence capturing a body part of the human individual, and to determine a plurality of regions-of-interest (ROIs) of the body part based on features of the body part;

a data science module to determine a set of bitplanes in the captured image sequence for each ROI that represent HC changes of the human individual using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set comprising bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets, the TOI module determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes;

a reconstruction module to determine intervals between heartbeats based on peaks in the HC change signal;

a stress module to determine heart rate variability using the intervals between heartbeats, and to determine a stress level using at least one determination of a standard deviation of the heart rate variability; and

an output module to output the stress level.

13. The system of claim 12 , wherein the cardiovascular data comprises hemodynamic changes received from an electrocardiograph.

14. The system of claim 12 , wherein the bitplanes are in a red channel, green channel, and blue channel of each image of the image sequence.

15. The system of claim 12 , wherein the body part is the individual's face.

16. The system of claim 15 , wherein determining a plurality of ROIs comprises tracking the human individual's face in each frame of the captured image sequence to track the ROIs.

17. The system of claim 12 , wherein the reconstruction module determines intervals between heartbeats by:

applying fast Fourier transform (FFT) and band pass filtering to determine a principle frequency component;

using the principle frequency component, reconstructing peaks of each heartbeat; and

determining intervals between the reconstructed peaks.

18. The system of claim 12 , wherein the stress module determines heart rate variability by generating a Poincare plot of the heartbeat intervals.

19. The system of claim 18 , wherein the stress module determines the stress level by:

determining a first standard deviation of points of heart rate variability in a direction perpendicular to a line of identity of the Poincare plot;

determining a first standard deviation of points of heart rate variability in a direction that is along the line of identity;

determining a measure of stress as a correlation to the second standard divided by the first standard deviation.

20. The system of claim 19 , the stress module determines the measure of stress by performing a Fisher z-transformation to the second standard divided by the first standard deviation.