Adaptive Heart Rate Estimation
Method, apparatus and system for estimating heart rate with a wearable device. The method includes receiving movement data and heart rate data, the movement data indicative of physical exertion of an individual associated with the wearable device, and the heart rate data measured for the individual during the same period; determining an estimated human power output based on the movement data indicative of physical exertion of the individual; determining a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter, wherein the heart rate estimate corresponds to the heart rate data, and the at least one adaptive parameter is adjustable based on the heart rate demand value and the heart rate estimate; and determining an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate.
1 . A method for estimating heart rate with a wearable device, comprising:
receiving movement data and heart rate data, wherein the movement data is indicative of physical exertion of an individual associated with the wearable device, and the heart rate data is measured for the individual during the same period;
determining an estimated human power output based on the movement data;
determining a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter, wherein the heart rate estimate corresponds to the heart rate data, and the at least one adaptive parameter is adjustable based on the heart rate demand value and the heart rate estimate; and
determining an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate.
2 . The method of claim 1 , wherein the movement data is associated with at least one of: acceleration, velocity, position, and altitude.
3 . The method of claim 1 , wherein receiving movement data and heart rate data comprises:
receiving, from a first sensor, movement data indicative of physical exertion of the individual associated with the wearable device, wherein the first sensor is at least one of an accelerometer, a barometric pressure sensor, and a Global Positioning System (GPS) sensor; and
receiving, from a second sensor, heart rate data measured for the individual during the same period, wherein the second sensor is at least one of an electrocardiography (ECG) sensor, a photoplethysmogram (PPG) sensor, a pulse oximeter, and an infrared (IR) sensor.
4 . The method of claim 1 , further comprising:
determining the improved heart rate estimate based on a predicted heart rate change and an improved heart rate estimate determined for a previous period, wherein the predicted heart rate change is determined based on the heart rate demand value and the at least one adaptive parameter.
5 . The method of claim 1 , wherein the at least one adaptive parameter comprises a scaling parameter and at least another parameter associated with a physiological profile learned from the individual.
6 . The method of claim 5 , wherein the physiological profile learned from the individual is associated with physical activity models corresponding to activities performed by the individual.
7 . The method of claim 1 , wherein determining an estimated human power output based on the movement data of the individual comprises:
determining an activity currently performed by the individual based on the movement data; and
selecting, based on the activity, a physical activity model for determining the estimated human power output.
8 . The method of claim 1 , wherein determining an estimated human power output based on the movement data comprises:
determining the estimated human power output based on a velocity, a gradient value and a mass value, wherein the mass value is associated with the individual, the gradient value is indicative of an inclination of terrain, and at least one of the velocity and the gradient value is derived from the movement data.
9 . The method of claim 1 , wherein determining a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter comprises:
determining the heart rate demand value based on the estimated human power output, a maximum heart rate, a rest heart rate, and a fitness level.
10 . The method of claim 9 , wherein the at least one adaptive parameter comprises a scaling parameter related to human power output produced at maximum oxygen effect.
11 . The method of claim 10 , wherein the fitness level is determined based on the estimated human power output and the scaling parameter, and the scaling parameter is adjusted for the individual over time.
12 . The method of claim 1 , wherein determining an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate comprises:
determining the improved heart rate estimate using the heart rate estimate and a heart rate estimate based on the heart rate demand value; and
adjusting the at least one adaptive parameter based on comparing the heart rate estimate and the heart rate estimate using adaptive learning.
13 . A wearable device, comprising:
a body configured to be coupled to a portion of an individual;
a non-transitory memory; and
a processor configured to execute instructions stored in the non-transitory memory to:
receive movement data and heart rate data, wherein the movement data is indicative of physical exertion of an individual associated with the wearable device, and the heart rate data is measured for the individual during the same period;
determine an estimated human power output based on the movement data;
determine a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter, wherein the heart rate estimate corresponds to the heart rate data, and the at least one adaptive parameter is adjustable based on the heart rate demand value and the heart rate estimate; and
determine an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate.
14 . The wearable device of claim 13 , wherein the instructions to receive movement data and heart rate data comprise instructions to:
receive, from a first sensor, movement data indicative of physical exertion of the individual associated with the wearable device, wherein the first sensor is at least one of an accelerometer, a barometric pressure sensor, and a Global Positioning System (GPS) sensor; and
receive, from a second sensor, heart rate data measured for the individual during the same period, wherein the second sensor is at least one of an electrocardiography (ECG) sensor, a photoplethysmogram (PPG) sensor, a pulse oximeter, and an infrared (IR) sensor.
15 . The wearable device of claim 13 , wherein the processor is further configured to execute instructions stored in the non-transitory memory to:
determine the improved heart rate estimate based on a predicted heart rate change and an improved heart rate estimate determined for a previous period, wherein the predicted heart rate change is determined based on the heart rate demand value and the at least one adaptive parameter.
16 . The wearable device of claim 13 , wherein the instructions to determine an estimated human power output based on the movement data comprise instructions to:
determine an activity currently performed by the individual based on the movement data; and
select, based on the activity, a physical activity model for determining the estimated human power output.
17 . The wearable device of claim 13 , wherein instructions to determine an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate comprise instructions to:
determine the improved heart rate estimate using the heart rate estimate and a heart rate estimate based on the heart rate demand value; and
adjust the at least one adaptive parameter based on comparing the heart rate estimate and the heart rate estimate using adaptive learning.
18 . A system, comprising:
a measurement component, comprising:
a body configured to be coupled to a portion of an individual;
a motion sensor coupled to the body, configured to measure movement data; and
a heart rate sensor coupled to the body, configured to measure heart rate data; and
an analysis component, comprising:
a non-transitory memory;
a processor configured to execute instructions stored in the non-transitory memory to:
receive the movement data and the heart rate data measured for the individual during the same period;
determine an estimated human power output based on the movement data;
determine a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter, wherein the heart rate estimate corresponds to the heart rate data, and the at least one adaptive parameter is adjustable based on the heart rate demand value and the heart rate estimate; and
determine an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate.
19 . The system of claim 18 , wherein the motion sensor is at least one of an accelerometer, a barometric pressure sensor, and a Global Positioning System (GPS) sensor, and the heart rate sensor is at least one of an electrocardiography (ECG) sensor, a photoplethysmogram (PPG) sensor, a pulse oximeter and an infrared (IR) sensor.
20 . The system of claim 18 , wherein the processor is further configured to execute instructions stored in the non-transitory memory to:
determine the improved heart rate estimate based on a predicted heart rate change and an improved heart rate estimate determined for a previous period, wherein the predicted heart rate change is determined based on the heart rate demand value and the at least one adaptive parameter.