GPS accuracy refinement using external sensors

• US 10,194,836 B2
• Filed: 03/20/2017
• Issued: 02/05/2019
• Est. Priority Date: 06/22/2012
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Venkatraman, Subramaniam, Panther, Heiko Gernot Albert, Yuen, Shelten Gee Jao
• Primary Examiner(s): McGue, Frank J
• Application Number:
  US15/464,018
• Publication Number:
  US 20170188893A1
• Time in Patent Office: 687 Days
• Field of Search: 702149
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0015 characterised by features of the telemetry system
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02427 Details of sensor
• CPC Class Codes: A61B 5/02433 for infrared radiation
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• 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: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/744 : Displaying an avatar, e.g. an animated cartoon character
• CPC Class Codes: A61B 8/06 : Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: G01S 19/14 : specially adapted for specific applications
• CPC Class Codes: G01S 19/49 : whereby the further system is an inertial position system, e.g. loosely-coupled
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/027 : using movement velocity, acceleration information
• CPC Class Codes: H04W 4/80 : Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Abstract

Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques for enhancing GPS speed and distance measurements based on step counts measured by a biometric monitoring device are provided. Such techniques may, in some implementations, involve blending two independently-measured data streams of raw distance traveled—one based on inter-coordinate GPS coordinate distances and one based on step count and stride length—using a Kalman filter to provide an enhanced-accuracy measurement of distance or speed traveled. In some other or additional implementations, distances or speeds calculated based on inter-coordinate GPS coordinate distances may be smoothed with a smoothing constant that is proportional to the variance in measured step count.

Claims

1. A biometric monitoring device to be worn on a person, comprising:

a global-positioning system (GPS) receiver;

a motion sensor;

a controller, communicatively coupled to the GPS receiver and the motion sensor, including at least one processor and a non-transitory computer readable medium, the non-transitory computer readable medium having computer-executable instructions stored thereon which, when executed, cause the at least one processor to;

obtain motion data from the motion sensor,

determine step rate data from the motion data for each of a plurality of measurement intervals,

determine a step rate variance from the step rate data for each of the measurement intervals,

obtain GPS data from the GPS receiver,

convert the GPS data into a plurality of first speed measurements respectively corresponding to the measurement intervals,

determine a refined speed measurement for each of the measurement intervals using both the first speed measurements and the step rate variances; and

a display configured to present one or more of the refined speed measurements.

2. The device of claim 1, wherein the computer-executable instructions, when executed, further cause the at least one processor to:

determine a smoothing constant for each of the measurement intervals based on the corresponding step rate variance,

wherein determining the refined speed measurements is further based on the smoothing constants.

3. The device of claim 2, wherein the computer-executable instructions, when executed, further cause the at least one processor to:

calculating a first one of the refined speed measurements using a filter having a time constant based on a first one of the smoothing constants corresponding to the first refined speed measurement,

wherein determining the refined speed measurements is further based on the first refines speed measurement.

4. The device of claim 3, wherein the filter is defined by the following equation:

RS=(1−

β

)·

RS_prev+β

·

S

where RS=the first refined speed measurement, RS_prev=a previous refined speed measurement of the refined speed measurements, S=a first one of the speed measurements corresponding to the first refined speed measurement, and β

=the first smoothing constant.

5. The device of claim 1, wherein determining the step rate variance for a first one of the measurement intervals is further based on the step rate data for a plurality of previous measurement intervals.

6. The device of claim 1, wherein the computer-executable instructions, when executed, further cause the at least one processor to:

determine that the step rate data for a first one of the measurement intervals is less than a threshold step rate; and

set a first refined speed measurement corresponding to the first measurement interval to zero in response to determining that the step rate data for the first one of the measurement intervals is less than the threshold step rate.

7. The device of claim 1, wherein the computer-executable instructions, when executed, further cause the at least one processor to:

convert the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals,

wherein converting the GPS data into the first speed measurements is further based on the first distance measurements.

8. The device of claim 1, wherein the computer-executable instructions, when executed, further cause the at least one processor to:

convert the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals;

determine step count data from the motion data for each of the measurement intervals; and

determine a refined distance measurement for each of the measurement intervals using both the first distance measurements and the step count data.

9. The device of claim 8, wherein each of the first distance measurements is based on a shortest distance between sequential coordinates in the GPS data during the corresponding measurement interval.

10. The device of claim 1, wherein the device is selected from the following:

a smartphone, a cell phone, a tablet, and a portable media device.

11. A method for refining speed measurements obtained from a biometric monitoring device, the biometric monitoring device comprising a global-positioning system (GPS) receiver, a motion sensor, a display, and a controller, the biometric monitoring device configured to be worn by a person, the method implemented by the controller and comprising:

obtaining motion data from the motion sensor;

determining step rate data from the motion data for each of a plurality of measurement intervals;

determining a step rate variance from the step rate data for each of the measurement intervals;

obtaining GPS data from the GPS receiver;

converting the GPS data into a plurality of first speed measurements respectively corresponding to the measurement intervals;

determining a refined speed measurement for each of the measurement intervals using both the first speed measurements and the step rate variances; and

sending instructions to the display to present one or more of the refined speed measurements.

12. The method of claim 11, further comprising:

determining a smoothing constant for each of the measurement intervals based on the corresponding step rate variance,

wherein determining the refined speed measurements is further based on the smoothing constants.

13. The method of claim 12, further comprising:

calculating a first one of the refined speed measurements using a filter having a time constant based on a first one of the smoothing constants corresponding to the first refined speed measurement,

wherein determining the refined speed measurements is further based on the first refines speed measurement.

14. The method of claim 13, wherein the filter is defined by the following equation:

RS=(1−

β

)·

RS_prev+β

·

S

where RS=the first refined speed measurement, RS_prev a previous refined speed measurement of the refined speed measurements, S=a first one of the speed measurements corresponding to the first refined speed measurement, and β

=the first smoothing constant.

15. The method of claim 11, wherein determining the step rate variance for a first one of the measurement intervals is further based on the step rate data for a plurality of previous measurement intervals.

16. The method of claim 11, further comprising:

determining that the step rate data for a first one of the measurement intervals is less than a threshold step rate; and

setting a first refined speed measurement corresponding to the first measurement interval to zero in response to determining that the step rate data for the first one of the measurement intervals is less than the threshold step rate.

17. The method of claim 11, further comprising:

converting the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals,

wherein converting the GPS data into the first speed measurements is further based on the first distance measurements.

18. The method of claim 11, further comprising:

converting the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals;

determining step count data from the motion data for each of the measurement intervals; and

determining a refined distance measurement for each of the measurement intervals using both the first distance measurements and the step count data.

19. The method of claim 18, wherein each of the first distance measurements is based on a shortest distance between sequential coordinates in the GPS data during the corresponding measurement interval.

20. The method of claim 11, wherein the biometric monitoring device is selected from the following:

a smartphone, a cell phone, a tablet, and a portable media device.

21. A non-transitory, machine-readable storage medium, having stored therein computer-executable instructions which, when executed by a processor in a biometric monitoring device to be worn on a person, cause the device to perform operations comprising:

obtaining motion data from a motion sensor of the biometric monitoring device;

determining step rate data from the motion data for each of a plurality of measurement intervals;

determining a step rate variance from the step rate data for each of the measurement intervals;

obtaining global positioning system (GPS) data from a GPS receiver of the biometric monitoring device;

converting the GPS data into a plurality of first speed measurements respectively corresponding to the measurement intervals;

determining a refined speed measurement for each of the measurement intervals using both the first speed measurements and the step rate variances; and

sending instructions to a display of the biometric monitoring device to present one or more of the refined speed measurements.

22. The non-transitory machine-readable storage medium of claim 21, further having stored therein computer-executable instructions which, when executed by the at least one processor, cause the device to perform operations comprising:

determining a smoothing constant for each of the measurement intervals based on the corresponding step rate variance,

wherein determining the refined speed measurements is further based on the smoothing constants.

23. The non-transitory machine-readable storage medium of claim 22, further having stored therein computer-executable instructions which, when executed by the at least one processor, cause the device to perform operations comprising:

calculating a first one of the refined speed measurements using a filter having a time constant based on a first one of the smoothing constants corresponding to the first refined speed measurement,

wherein determining the refined speed measurements is further based on the first refines speed measurement.

24. The non-transitory machine-readable storage medium of claim 23, wherein the filter is defined by the following equation:

RS=(1−

β

)·

RS_prev+β

·

S

where RS=the first refined speed measurement, RS_prev a previous refined speed measurement of the refined speed measurements, S=a first one of the speed measurements corresponding to the first refined speed measurement, and β

=the first smoothing constant.

25. The non-transitory machine-readable storage medium of claim 21, wherein determining the step rate variance for a first one of the measurement intervals is further based on the step rate data for a plurality of previous measurement intervals.

26. The non-transitory machine-readable storage medium of claim 21, further having stored therein computer-executable instructions which, when executed by the at least one processor, cause the device to perform operations comprising:

determining that the step rate data for a first one of the measurement intervals is less than a threshold step rate; and

setting a first refined speed measurement corresponding to the first measurement interval to zero in response to determining that the step rate data for the first one of the measurement intervals is less than the threshold step rate.

27. The non-transitory machine-readable storage medium of claim 21, further having stored therein computer-executable instructions which, when executed by the at least one processor, cause the device to perform operations comprising:

converting the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals,

wherein converting the GPS data into the first speed measurements is further based on the first distance measurements.

28. The non-transitory machine-readable storage medium of claim 21, further having stored therein computer-executable instructions which, when executed by the at least one processor, cause the device to perform operations comprising:

converting the GPS data into a plurality of first distance measurements respectively corresponding to the measurement intervals;

determining step count data from the motion data for each of the measurement intervals; and

determining a refined distance measurement for each of the measurement intervals using both the first distance measurements and the step count data.

29. The non-transitory machine-readable storage medium of claim 28, wherein each of the first distance measurements is based on a shortest distance between sequential coordinates in the GPS data during the corresponding measurement interval.

30. The non-transitory machine-readable storage medium of claim 21, wherein the biometric monitoring device is selected from the following:

a smartphone, a cell phone, a tablet, and a portable media device.