Adaptive lifestyle metric estimation

• US 9,874,457 B2
• Filed: 05/30/2014
• Issued: 01/23/2018
• Est. Priority Date: 05/30/2014
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Fung, Han Yee Mimi, Albadawi, Haithem
• Primary Examiner(s): Cosimano, Edward
• Application Number:
  US14/292,078
• Publication Number:
  US 20150345985A1
• Time in Patent Office: 1,334 Days
• Field of Search: None
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01C 25/00 Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass testing, calibrating or compensating compasses G01C17/38
• CPC Class Codes: G01D 21/00 Measuring or testing not otherwise provided for
• CPC Class Codes: G01P 15/02 by making use of inertia forces using solid seismic massesG01P15/14 takes precedence
• CPC Class Codes: G01P 15/08 with conversion into electric or magnetic values
• CPC Class Codes: G04G 21/02 Detectors of external physical values, e.g. temperature
• CPC Class Codes: G04G 21/025 for measuring physiological data
• CPC Class Codes: G06F 1/3206 Monitoring of events, devices or parameters that trigger a change in power modality
• CPC Class Codes: G06F 17/40 Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

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

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.