Statistical heart rate monitoring for estimating calorie expenditure

1. A method of calibrating a fitness tracking device for calorie estimation for a specific exercise session, the method comprising:

determining, by a wearable computing device, a start of an exercise session associated with a user of the wearable computing device, the wearable computing device including a processor, a memory, a heart rate sensor, and at least one of a motion sensing module, a display module, and an interface module;

in response to determining the start of the exercise session, measuring, by the heart rate sensor of the wearable computing device, heart rate data (HR) for a first period of time, the first period of time following the start of the exercise session;

determining, by the processor of the wearable computing device, an onset heart rate value of the user based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time, the onset heart rate value being different from a calibrated resting heart rate (RHR) for the user stored in the memory;

associating, by the processor of the wearable computing device, an RHR parameter of a calorimetry model to the onset heart rate value to calibrate the wearable computing device for the exercise session; and

estimating, by the processor of the wearable computing device, energy expenditure of the user during a second period of time based on the calorimetry model and a plurality of heart rate measurements (HR) obtained by the wearable computing device during the second period of time, the second period of time associated with a portion of time after the first period of time and prior to an end of the exercise session.

2. The method of claim 1 , wherein estimating the energy expenditure further comprises calculating a fraction of heart rate reserve (FHR) based on maximum heart rate (HR max ), wherein:

FHR=(HR max −HR)/(HR max −RHR).

3. The method of claim 2 , wherein the energy expenditure is proportional to a function ƒ(FHR), wherein ƒ(FHR) is approximately a sigmoidal nonlinearity.

4. The method of claim 3 , wherein ƒ(0)=1 and ƒ(1)=0.

5. The method of claim 2 , wherein HR max is based on at least one of:

an age of the user; and

at least one of a previously measured heart rate.

6. The method of claim 1 , wherein the RHR for the user stored in the memory data comprises a heart rate value based on a maximum oxygen exchange rate of the user (VO 2 max) and a maximum heart rate of the user (HR max ), and VO 2 max comprises one of a default value or a value previously calibrated by the user.

7. The method of claim 6 , wherein the (HR max ) comprises at least one of:

a maximum heart rate value of the at least one of a previously measured heart rate;

a percentile of the at least one of a previously measured heart rate;

a percentile of the at least one of a previously measured heart rate above a first threshold heart rate value;

a percentile of the at least one of a previously measured heart rate above a HR max based on the age of the user; and

a maximum heart rate value being less than a percentage of the HR max based on an age of the user.

8. The method of claim 7 , wherein:

the percentile of the at least one of a previously measured heart rate is approximately a 98 th percentile;

the percentile of the at least one of a previously measured heart rate above a first threshold heart rate value is approximately a 98 th percentile;

the percentile of the at least one of a previously measured heart rate above a HR max based on the age of the user is approximately a 98 th percentile; and

the percentage of the HR max based on the age of the user is approximately 110%.

9. The method of claim 1 , wherein the calibrated RHR includes at least one of:

a preset value; and

a RHR based on user biometric data.

10. The method of claim 9 , wherein the preset RHR comprises at least one of:

a minimum value of a list of RHR values from prior exercise sessions;

a percentile of the list of RHR values from prior exercise sessions; and

a percentile of RHR values in the list below a second threshold heart rate value.

11. The method of claim 10 , wherein:

the second threshold heart rate value comprises 90 beats per minute.

12. The method of claim 9 , wherein:

the preset value comprises 72 beats per minute; and

the RHR based on user biometric data comprises a heart rate value based on a maximum oxygen exchange rate of the user (VO 2 max) and a maximum heart rate of the user (HR max ).

13. The method of claim 1 , wherein the first period of time comprises 10 seconds to 1 minute.

14. The method of claim 1 , wherein the first period of time is associated with a timer, wherein the timer activates at least one of a low-power mode of heart rate sensing and a high-power mode of heart rate sensing.

15. A system for tracking physiological states and parameters for calorie estimation and configured to be calibrated for a specific exercise session, the system comprising:

a wearable computing device, the wearable computing device including a processor, a memory, a heart rate sensor, and at least one of a motion sensing module, a display module, and an interface module, the wearable computing device configured to:

determine a start of an exercise session associated with a user of the wearable computing device;

in response to determining the start of the exercise session, measure heart rate data (HR) for a first period of time, the first period of time following the start of the exercise session;

determine an onset heart rate value of the user based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time, the onset heart rate value being different from a calibrated resting heart rate (RHR) for the user stored in the memory;

associate an RHR parameter of a calorimetry model to the onset heart rate value to calibrate the wearable computing device for the exercise session; and

estimate energy expenditure of the user during a second period of time based on the calorimetry model and a plurality of heart rate measurements (HR) obtained by the wearable computing device during the second period of time, the second period of time associated with a portion of time after the first period of time and prior to an end of the exercise session.

16. The system of claim 15 , wherein estimating the energy expenditure further comprises calculating a fraction of heart rate reserve (FHR) based on maximum heart rate (HR max ), wherein:

FHR=(HR max −HR)/(HR max −RHR).

17. The system of claim 16 , wherein HR max is based on at least one of:

an age of the user; and

at least one of a previously measured heart rate.

18. The system of claim 17 , wherein the at least one of a previously measured heart rate comprises at least one of:

a maximum heart rate value of the at least one of a previously measured heart rate;

a percentile of the at least one of a previously measured heart rate;

a percentile of the at least one of a previously measured heart rate above a first threshold heart rate value;

a percentile of the at least one of a previously measured heart rate above a HR max based on the age of the user; and

a maximum heart rate value being less than a percentage of the HR max based on the age of the user.

19. The system of claim 15 , wherein the calibrated RHR includes at least one of:

a preset value; and

a RHR based on user biometric data.

20. The system of claim 19 , wherein the preset RHR comprises at least one of:

a minimum value of a list of RHR values from prior exercise sessions;

a percentile of the list of RHR values from prior exercise sessions; and

a percentile of RHR values in the list below a second threshold heart rate value.