Context-aware heart rate estimation

Patent metadata

Field	Value
Publication number	US-9848823-B2
Application number	US-201514812870-A
Country	US
Kind code	B2
Filing date	Jul 29, 2015
Priority date	May 29, 2014
Publication date	Dec 26, 2017
Grant date	Dec 26, 2017

Abstract

A device can estimate the heart rate of an active user by using a physiological model to refine a “direct” measurement of the user's heart rate obtained using a pulse sensor. The physiological model can be based on heart rate response to activity and can be informed by context information, such as the user's current activity and/or intensity level as well as user-specific parameters such as age, gender, general fitness level, previous heart rate measurements, etc. The physiological model can be used to predict a heart rate, and the prediction can be used to assess or improve the direct measurement.

First claim

What is claimed is:

1. A method of determining a heart rate of a user, the method comprising, by a wearable device:
generating, using a pulse sensor of the wearable device, a sequence of heart rate data samples;
determining, using the sequence of heart rate data samples, a direct heart rate estimate;
determining an activity context of the wearable device, the activity context including a physical activity in which the user is engaged and an intensity of the physical activity;
generating, based in part on the activity context and in part on a user-specific parameter, a modeled heart rate estimate, the modeled heart rate estimate being based on a physiological model of heart rate response to the activity context and generated independently of the sequence of heart rate data samples; and
determining a final heart rate estimate based on the direct heart rate estimate and the modeled heart rate estimate.

2. The method of claim 1 wherein determining the direct heart rate estimate includes:
determining, using a motion sensor of the wearable device, a motion-related noise component; and
subtracting the motion-related noise component from the heart rate data samples.

3. The method of claim 1 wherein the pulse sensor includes a photoplethysmographic sensor and wherein determining the direct heart rate estimate includes:
measuring a dark channel signal for the photoplethysmographic sensor; and
subtracting the dark channel signal from the heart rate data samples.

4. The method of claim 1 wherein determining the direct heart rate estimate includes:
generating a frequency spectrum from the heart rate data samples;
comparing the generated frequency spectrum to each of a plurality of template spectra, each template spectrum corresponding to a different candidate heart rate; and
identifying a best matching template spectrum based on the comparing,
wherein the direct heart rate estimate is based on the best-matching template spectrum.

5. The method of claim 4 further comprising:
generating a confidence score for the direct heart rate estimate, the confidence score based at least in part on how well the generated frequency spectrum matches the best-matching template spectrum,
wherein determining the final heart rate estimate is based in part on the confidence score.

6. The method of claim 1 wherein the activity context includes an activity identifier and wherein generating the modeled heart rate estimate includes:
determining a starting time based on a most recent change in the activity identifier;
establishing a starting heart rate based on a heart rate determined prior to the starting time;
determining an equilibrium heart rate based at least in part on a current activity indicated by the activity identifier; and
applying a filter function to the starting heart rate and the equilibrium heart rate based on a current time and the starting time.

7. The method of claim 6 wherein the equilibrium heart rate is determined based in part on the current activity and in part on a user-specific parameter.

8. The method of claim 7 wherein the user-specific parameter includes a parameter indicating a demographic characteristic of the user.

9. The method of claim 7 wherein the user-specific parameter includes a parameter indicating a fitness level of the user.

10. The method of claim 7 wherein the user-specific parameter includes a previously determined user-specific equilibrium heart rate associated with the current activity.

11. The method of claim 1 wherein generating the modeled heart rate estimate includes generating a probability mass function indicating a probability of each of a plurality of possible heart rates,
wherein determining the final heart rate estimate is based in part on the probability mass function.

12. A device comprising:
a pulse sensor;
a motion sensor; and
a processor coupled to the pulse sensor and the motion sensor, the processor configured to:
obtain a sequence of heart rate data samples from the pulse sensor;
determine, based on the sequence of heart rate data samples, a direct heart rate estimate;
determine, based at least in part on data from the motion sensor, an activity context of the device, the activity context including a physical activity in which a user is engaged and an intensity of the physical activity;
generate, based in part on the activity context of the device and in part on a user-specific parameter, a modeled heart rate estimate, the modeled heart rate estimate being based on a physiological model of heart rate response to the activity context and generated independently of the sequence of heart rate data samples; and
determine a final heart rate estimate based on the direct heart rate estimate and the modeled heart rate estimate.

13. The device of claim 12 wherein the pulse sensor includes a photoplethysmographic (PPG) sensor.

14. The device of claim 12 wherein the motion sensor includes one or more of an accelerometer or a gyroscope.

15. The device of claim 12 further comprising a user interface coupled to the processor and operable to present the final heart rate estimate to the user.

16. The device of claim 12 wherein the physiological model includes a filter function to model a heart rate transition from a staring heart rate to an equilibrium heart rate associated with a current activity context of the device.

17. A computer-readable storage medium having stored thereon program instructions that, when executed by a processor, cause the processor to perform a method comprising:
obtaining a sequence of heart rate data samples for a user;
determining, using the sequence of heart rate data samples, a direct heart rate estimate;
determining an activity context for the user, the activity context including a physical activity in which the user is engaged and an intensity of the physical activity;
generating, based in part on the activity context and in part on a user-specific parameter, a modeled heart rate estimate, the modeled heart rate estimate being based on a physiological model of heart rate response to the activity context and generated independently of the sequence of heart rate data samples; and
determining a final heart rate estimate based at least in part on the direct heart rate estimate and the modeled heart rate estimate.

18. The computer-readable storage medium of claim 17 wherein the method further comprises:
updating the user-specific parameter based on the final heart rate estimate.

19. The computer-readable storage medium of claim 17 wherein the method further comprises:
determining a confidence score for the direct heart rate estimate; and
determining a probability mass function for the modeled heart rate estimate,
wherein the final heart rate estimate is based in part on the confidence score and the probability mass function.

Assignees

• Apple Inc

Inventors

• Raghuram Karthik Jayaraman
• Silva James M
• Kanaris Alexander
• Bhaskaran Barker N

Classifications

• A61B5/02438

  with portable devices, e.g. worn by the patient · CPC title

• A61B5/486 Primary

  Biofeedback (using electroencephalography [EEG] A61B5/375) · CPC title

• A61B5/1118

  Determining activity level · CPC title

• A61B5/721

  using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured · CPC title

• A61B5/0205

  Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition · CPC title