IP Library › Granted Patent US 9,874,457
Granted Patent B2
US 9,874,457 · App. 14/292,078 · Granted Jan 23, 2018

Adaptive lifestyle metric estimation

Inventors: Han Yee Mimi Fung (Bellevue, WA); Haithem Albadawi (Redmond, WA)
Assignee: MICROSOFT TECHNOLOGY LICENSING, LLC
G01C22/006A61B5/02438A61B5/0531A61B5/1118A61B5/681G01C21/165G01C25/00G01P15/02G04G21/02G04G21/025G06F1/3206G06F19/3406G06F19/3431A61B5/01A61B5/11A61B5/4866A61B5/6898A61B2560/0209A61B2562/0219G01D21/00G01P15/08G06F17/40G06F19/00
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Quick Facts
Patent No.
US 9,874,457
App. No.
14/292,078
Granted
Jan 23, 2018
Kind
B2
Abstract

Systems and methods for estimating lifestyle metrics with a wearable electronic device are disclosed herein. One disclosed system may include the wearable electronic device comprising a processor and a sensor system providing inputs to the processor. The sensor system may include a high power sensor and a low power sensor. The processor may operate in a high power mode in which both sensors are operational and a low power mode in which the high power sensor is not operational. In the high power mode, the processor may compute a lifestyle metric about a user for a first time period based on first data from the high power sensor. In the low power mode, the processor may compute the lifestyle metric for a second time period based on second data from the low power sensor and the first data and/or a derivative of the first data.

Claims (49)

1. A wearable electronic device switchable between power modes in response to a user interaction with the device or a signal from a sensor of the device, the device comprising:

a processor; and

a sensor system providing inputs to the processor, the sensor system including a first sensor and a second sensor, the first sensor being a high power sensor that consumes a greater amount of power when in operation than the second sensor, which is a low power sensor;

wherein the processor is configured to operate in a high power mode in which both sensors are operational and a low power mode in which the low power sensor is operational and the high power sensor is not operational;

wherein, in the high power mode, the processor is configured to compute a lifestyle metric about a user for a first time period based on first data from the high power sensor; and

wherein, in the low power mode, the processor is configured to compute the lifestyle metric for a second time period based on:

second data from the low power sensor; and

the first data and/or a derivative of the first data.

2. The wearable electronic device of claim 1 , further comprising a band wearable on a wrist of the user.

3. The wearable electronic device of claim 1 , wherein the high power sensor is a global positioning system (GPS) receiver and the low power sensor is an accelerometer.

4. The wearable electronic device of claim 3 , wherein the lifestyle metric is a distance traveled by the user and the derivative of the first data is a stride length of the user.

5. The wearable electronic device of claim 4 , wherein the processor is further configured to sum the distance traveled by the user and a previous total distance to obtain a current total distance.

6. The wearable electronic device of claim 4 , wherein the stride length is one of a plurality of stride lengths, each respective stride length being calculated for an associated gait of the user.

7. The wearable electronic device of claim 1 , wherein the high power sensor is a heart rate sensor and the low power sensor is an accelerometer.

8. The wearable electronic device of claim 7 , wherein the lifestyle metric is a calorie expenditure and the derivative of the first data comprises a first derivative and a second derivative, the first derivative being a resting heart rate and the second derivative being a recovery rate.

9. The wearable electronic device of claim 8 , wherein the processor is further configured to compute the resting heart rate by:

collecting the first data while the user is sleeping;

filtering out first data outside a heart rate range; and

computing the resting heart rate based on the filtered first data.

10. The wearable electronic device of claim 8 , wherein the processor is further configured to operate in a remote mode in which the high power sensor is a remote high power sensor in a remote device, wherein the processor is configured to:

receive remote data from the remote high power sensor; and

compute the lifestyle metric based on the remote data.

11. A method for estimating lifestyle metrics with a wearable electronic device switchable between power modes in response to a user interaction with the device or a signal from a sensor of the device, the method comprising:

operating in a high power mode in which a first sensor and a second sensor are operational, the first sensor being a high power sensor that consumes a greater amount of power when in operation than the second sensor, which is a low power sensor;

in the high power mode, computing a lifestyle metric about a user for a first time period based on first data from the high power sensor;

operating in a low power mode in which the high power sensor is not operational and the low power sensor is operational; and

in the low power mode, computing the lifestyle metric for a second time period based on:

second data from the low power sensor; and

the first data and/or a derivative of the first data.

12. The method of claim 11 , wherein the wearable electronic device further comprises a band wearable on a wrist of the user.

13. The method of claim 11 , wherein the high power sensor is a heart rate sensor and the low power sensor is an accelerometer.

14. The method of claim 13 , wherein the lifestyle metric is a calorie expenditure and the derivative of the first data comprises a first derivative and a second derivative, the first derivative being a resting heart rate and the second derivative being a recovery rate.

15. The method of claim 14 , further comprising computing the resting heart rate by:

collecting the first data while the user is sleeping;

filtering out first data outside a heart rate range; and

computing the resting heart rate based on the filtered first data.

16. The method of claim 11 , wherein the high power sensor is a global positioning system (GPS) receiver and the low power sensor is an accelerometer.

17. The method of claim 16 , wherein the lifestyle metric is a distance traveled by the user and the derivative of the first data is a stride length of the user.

18. The method of claim 17 , further comprising summing the distance traveled by the user and a previous total distance to obtain a current total distance.

19. The method of claim 17 , wherein the stride length is one of a plurality of stride lengths, each respective stride length being calculated for an associated gait of the user.

20. A wearable electronic device switchable between power modes in response to a user interaction with the device or a signal from a sensor of the device, the device comprising:

a processor;

a sensor system providing inputs to the processor, the sensor system including a first sensor and a second sensor, the first sensor being a high power sensor that consumes a greater amount of power when in operation than the second sensor, which is a low power sensor, wherein the high power sensor is a global positioning system (GPS) receiver and the low power sensor is an accelerometer; and

a band wearable on a wrist of a user;

wherein the processor is configured to operate in a high power mode in which both sensors are operational and a low power mode in which the low power sensor is operational and the high power sensor is not operational; wherein

in the high power mode, the processor is configured to compute a lifestyle metric about the user for a first time period based on first data from the high power sensor; and

in the low power mode, the processor is configured to compute the lifestyle metric for a second time period based on:

second data from the low power sensor; and

the first data and/or a derivative of the first data.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2015
From: MICROSOFT CORPORATION
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 039025/0454 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 25, 2014
From: FUNG, HAN YEE MIMI; ALBADAWI, HAITHEM
To: MICROSOFT CORPORATION
Reel/Frame 033180/0359 →
Continuity (1)
Related Publication 20150345985A1 · Dec 3, 2015