Measuring psychological stress from cardiovascular and activity signals
A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.
1. A system for measuring psychological stress, the system comprising:
a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor; and
a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to:
determine R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN);
determine a stress feature (SF) using the SDNN by calculating a mean heart rate (HR) from the ECG within a predetermined time period and by utilizing an algorithm SF =HR+α*SDNN, wherein α is a predetermined negative variable that allows for combining HR and SDNN;
in response to reaching a threshold, perform adaptation to update a probability mass function (PMF); and
determine a stress level (SL) using the SF and the updated PMF to measure the psychological stress.
2. The system of claim 1 , wherein the application further causes the processor to:
determine a posture state, wherein the psychological stress is not measured if the posture state is active; and
display the determined SL to a user or another device.
3. The system of claim 2 , wherein the posture state includes any of active, sitting, and standing.
4. The system of claim 1 , wherein to determine R-R intervals from the ECG to calculate the SDNN further comprises to:
detect R peaks from a measured ECG within the predetermined time period; and
calculate R-R intervals using the detected R peaks.
5. The system of claim 1 , wherein to perform adaptation to update the PMF further comprises to:
group data into a predetermined distribution;
calibrate the predetermined distribution according to a detected resting heart rate; and
adjust the predetermined distribution according to additional samples received.
6. The system of claim 5 , wherein to adjust the predetermined distribution according to additional samples received further comprises to:
in response to data arriving, multiply all bins of the predetermined distribution by 1-ε; and
add ε to a bin corresponding to the data, wherein ε is a parameter for how much the PMF is changed with each adaptation.
7. The system of claim 5 , wherein to determine the stress level (SL) using the SF and the PMF further comprises to:
add all bins below a bin corresponding to the SF; and
compute the SL utilizing an algorithm that includes a probability mass function for a given posture (PMF posture ), the SF, and the added bins.
8. The system of claim 7 , wherein the application further causes the processor to:
add a fraction of a current bin to improve granularity.