STATISTICAL HEART RATE MONITORING FOR ESTIMATING CALORIE EXPENDITURE

• US 20160256058A1
• Filed: 03/04/2016
• Published: 09/08/2016
• Est. Priority Date: 03/06/2015
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: PHAM, Hung A., MERMEL, Craig, SINGH ALVARADO, Alexander, TAN, Xing, MOORE, Richard Channing III, RAGHURAM, Karthik Jayaraman, ULLAL, Adeeti
• Granted Patent: US 10,244,948 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence

Abstract

Systems and methods are disclosed for tracking physiological states and parameters for calorie estimation. A start of an exercise session associated with a user of a wearable computing device is determined. Heart rate data is measured for a first period of time. An onset heart rate value of the user is determined 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. A resting heart rate parameter (RHR) of a calorimetry model is associated with at least one of the onset heart rate value, a preset RHR, and an RHR based on user biometric data. Energy expenditure of the user during a second period of time is estimated based on the calorimetry model and a plurality of heart rate measurements obtained by the wearable computing device during the second period of time.

Claims

1. A method of improving a fitness tracking device such that the fitness tracking device tracks physiological states and parameters for calorie estimation, 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 heart rate sensor, and at least one of a motion sensing module, a display module, and an interface module;

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 associated with 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;

associating, by the processor of the wearable computing device, a resting heart rate parameter (RHR) of a calorimetry model to at least one of;

the onset heart rate value,

a preset RHR, and

an RHR based on user biometric data; 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), 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 {dot over (V)}O₂max comprises one of a default value or a value previously calibrated by the user.

7. The method of claim 6, 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.

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 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.

10. The method of claim 9, wherein:

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

11. The method of claim 1, wherein the RHR is further associated with the lower of:

the preset value; and

the RHR based on user biometric data.

12. The method of claim 11, 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 ({dot over (V)}O₂max) 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, the system comprising:

a wearable computing device, the wearable computing device including a processor, 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,

measure heart rate data (HR) for a first period of time, the first period of time associated with 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;

associate a resting heart rate parameter (RHR) of a calorimetry model to at least one of;

the onset heart rate value,

a preset RHR, and

an RHR based on user biometric data; 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), wherein:

FHR=(HR_max−

HR)/(HR_max−

RHR).

17. The system of claim 15, 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 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.

20. The system of claim 15, wherein the RHR is further associated with the lower of:

the preset value; and

the RHR based on user biometric data.