A wearable device includes one or more biometric sensors configured to determine biometric data of a wearer of the wearable device and a stress assessment tool. The stress assessment tool is configured to determine a periodic stress score, via a machine-learning model, based at least on the biometric data, visually present, via a display associated with the wearable device, a graphical user interface (GUI) including the periodic stress score, receive wearer feedback evaluating the accuracy of the periodic stress score, adjust the machine-learning model based on the wearer feedback, determine a reassessed periodic stress score, via the machine-learning model, and visually present, via the display, the reassessed periodic stress score in the GUI.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A wearable device comprising: one or more biometric sensors configured to determine biometric data of a wearer of the wearable device; and a stress assessment tool configured to: determine a periodic stress score of the wearer for a designated period, via a machine-learning model based at least on the biometric data, visually present, via a display associated with the wearable device, a graphical user interface (GUI) including the periodic stress score, receive wearer feedback evaluating the accuracy of the periodic stress score, adjust the machine-learning model based on the wearer feedback, determine a reassessed periodic stress score via the machine-learning model, and visually present, via the display, the reassessed periodic stress score in the GUI. 2. The wearable device of claim 1, wherein the stress assessment tool is configured to determine the reassessed periodic stress score at least once per day, and visually present, via the display, the reassessed periodic stress score in the GUI at least once per day. 3. The wearable device of claim 1, wherein the periodic stress score is selected from a scale of different, predetermined stress scores. 4. The wearable device of claim 1, wherein the biometric data includes heart rate information. 5. The wearable device of claim 1, wherein the periodic stress score is determined, via the machine-learning model, further based on activity data of the wearer. 6. The wearable device of claim 5, wherein the activity data includes one or more of sleep data, workout data, and calendar data. 7. The wearable device of claim 5, wherein the activity data includes activities of the wearer scheduled in a calendar, and wherein the stress assessment tool is configured to: determine a predicted periodic stress score, via the machine-learning model, based at least on future activities scheduled in the calendar, and visually present, via the display, the predicted periodic stress score in the GUI. 8. The wearable device of claim 1, wherein the machine-learning model is selected from a group consisting of a random decision forest regression model and a linear regression model. 9. The wearable device of claim 1, wherein the wearer feedback includes manual adjustment of the periodic stress score to a different periodic stress score. 10. The wearable device of claim 1, wherein the stress assessment tool is configured to: visually present, via the display, an intervention activity to reduce the periodic stress score of the wearer in the GUI, track whether the wearer performs the intervention activity, and visually present, via the display, a visual representation of an effect of the intervention activity on the periodic stress score of the wearer in the GUI. 11. The wearable device of claim 1, further comprising: a location sensor configured to determine a geographic location of the wearable device; and wherein the stress assessment tool is configured to: determine one or more stress-related locations based at least on the biometric data and the geographic location; and visually present, via the display, a map including visual markers indicating the one or more stress-related locations in the GUI. 12. A method of measuring a periodic stress score of a user with a computing device, the method comprising: receiving biometric data of the user from one or more biometric sensors; determining a periodic stress score of the user for a designated period, via a machine-learning model, based at least on the biometric data; visually presenting, via a display, a graphical user interface (GUI) including the periodic stress score; receiving user feedback evaluating the accuracy of the periodic stress score; adjusting the machine-learning model based on the user feedback; determining a reassessed periodic stress score via the machine-learning model; and visually presenting, via the display, the reassessed periodic stress score in the GUI. 13. The method of claim 12, wherein the periodic stress score is determined, via the machine-learning model, further based on activity data of the user including activities of the user scheduled in a calendar, and wherein the method further comprises: determining a predicted periodic stress score, via the machine-learning model, based at least on future activities scheduled in the calendar; and visually presenting, via the display, the predicted periodic stress score in the GUI. 14. The method of claim 12, further comprising: visually presenting, via the display, an intervention activity to reduce the periodic stress score of the user in the GUI; tracking whether the user performs the intervention activity; and visually presenting, via the display, a visual representation of an effect of the intervention activity on the periodic stress score of the user in the GUI. 15. The method of claim 12, further comprising: determining, via a location sensor, a geographic location of the user; determining one or more stress-related locations based at least on the biometric data and the geographic location; and visually presenting, via the display, a map including visual markers indicating the one or more stress-related locations in the GUI. 16. The method of claim 12, wherein the user feedback includes manual adjustment of the periodic stress score to a different periodic stress score. 17. A wearable device comprising: one or more biometric sensors configured to determine biometric data of a wearer of the wearable device: and a stress assessment tool configured to: receive activity data including activities of the wearer scheduled in a calendar, determine a periodic stress score of the wearer for a designated period, via a machine-learning model, based at least on the biometric data and the activity data, visually present, via a display associated with the wearable device, a graphical user interface (GUI) including the periodic stress score, determine a predicted periodic stress score, via the machine-learning model, based at least on future activities scheduled in the calendar, and visually present, via the display, the predicted periodic stress score in the GUI. 18. The wearable device of claim 17, wherein the stress assessment tool is configured to: receive wearer feedback evaluating the accuracy of the periodic stress score, adjust the machine-learning model based on the wearer feedback, determine a reassessed periodic stress score, via the machine-learning model, and visually present, via the display, the reassessed periodic stress score in the GUI. 19. The wearable device of claim 17, wherein the stress assessment tool is configured to: visually present, via the display, an intervention activity to reduce the periodic stress score of the wearer in the GUI, track whether the wearer performs the intervention activity, and visually present, via the display, a visual representation of an effect of the intervention activity on the periodic stress score of the wearer in the GUI. 20. The wearable device of claim 17, further comprising: a location sensor configured to determine a geographic location of the wearable device; and wherein the stress assessment tool is configured to: determine one or more stress-related locations based at least on the biometric data and the geographic location; and visually present, via the display, a map including visual markers indicating the one or more stress-related locations in the GUI.