MACHINE LEARNING MODEL FOR DYNAMIC STRESS SCORING
A stress score from a physiological monitor provides a local, objective, quantitative measurement of stress in a numerical form that can be used as the basis for real time coaching, decision-making, and so forth.
1 . A computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:
providing a machine learning model trained to report a stress level based on a heart rate, a heart rate variability, and a motion measured from a monitor;
acquiring a plurality of measurements of the stress level based on data from the monitor for a user over an interval;
processing the plurality of measurements of the stress level over the interval to provide a stress estimate for the interval;
scaling the stress estimate with a function that transforms the stress estimate into a value within a predetermined range; and
presenting the value to the user in a display as a dynamic stress value.
2 . A method comprising:
providing a machine learning model trained to report a stress level based on a heart rate, a heart rate variability, and a motion measured from a monitor;
acquiring a plurality of measurements of the stress level based on data from the monitor for a user over an interval; and
processing the plurality of measurements of the stress level over the interval to provide a stress estimate for the interval.
3 . The method of claim 2 further comprising:
scaling the stress estimate with a function that transforms the stress estimate into a value within a predetermined range; and
presenting the value to the user in a display as a dynamic stress value.
4 . The method of claim 3 , further comprising displaying the dynamic stress value on a wearable monitor.
5 . The method of claim 3 , further comprising displaying the dynamic stress value on a user device.
6 . The method of claim 3 , wherein the function includes a non-linear scaling that transforms a majority of a number of stress estimates for an individual to a lowest value for the dynamic stress value.
7 . The method of claim 2 , further comprising generating an intervention recommendation for the user based on the stress estimate.
8 . The method of claim 7 , wherein the intervention recommendation includes a real time recommendation based on a current stress estimate.
9 . The method of claim 7 , wherein the intervention recommendation includes a real time recommendation based on a current activity.
10 . The method of claim 2 , wherein the machine learning model is trained using a training set that includes a set of measured physiological responses to one or more predetermined stressors for a plurality of users, each tagged with a stress score from a corresponding one of the plurality of users when exposed to a corresponding one of the one or more predetermined stressors.
11 . The method of claim 2 , further comprising identifying a threshold for the stress estimate that is indicative of acute stress.
12 . The method of claim 11 , further comprising reporting the acute stress to the user.
13 . The method of claim 11 , further comprising recommending a remediation for the acute stress to the user.
14 . The method of claim 2 , further comprising identifying a threshold for the stress estimate that is indicative of autonomic activation.
15 . The method of claim 2 , wherein the plurality of measurements of the stress level include measurements at least every thirty seconds.
16 . The method of claim 2 , wherein the interval is between three minutes and ten minutes.
17 . A method comprising:
providing a model configured to output a stress level based on a heart rate, a heart rate variability, and a motion measured from a monitor,
acquiring a plurality of measurements of the stress level based on data from the monitor for a user over an interval; and
processing, using the model, the plurality of measurements of the stress level over the interval to provide a stress estimate for the interval.
18 . The method of claim 17 , wherein the model includes a machine learning model trained to report the stress level based on the heart rate, the heart rate variability, and the motion measured from the monitor.
19 . The method of claim 17 , wherein the model includes an analytical model using a combination of a scaled heart rate score and a scaled heart rate variability score.
20 . The method of claim 19 , wherein the analytical model weights a contribution of the scaled heart rate score and the scaled heart rate variability score based on motion detected by the monitor.