GPS accuracy refinement using external sensors

• US 9,597,014 B2
• Filed: 05/29/2014
• Issued: 03/21/2017
• 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:
  US14/290,912
• Publication Number:
  US 20140316305A1
• Time in Patent Office: 1,027 Days
• Field of Search: 342/357.3
• US Class Current: 1/1
• 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. An apparatus comprising:

a global-positioning system (GPS) receiver;

at least one motion sensor;

control logic, the control logic configured to;

(a) obtain GPS data from the GPS receiver,

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

(c) obtain data from the at least one motion sensor,

(d) determine step count data from the data obtained from the at least one motion sensor for each of the measurement intervals, and

(e) determine a refined distance measurement for each of the measurement intervals using both the first distance measurements and the step count data, wherein the refined distance measurements indicate the distance traveled by a user of the apparatus; and

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

2. The apparatus of claim 1, wherein the refined distance measurements for each measurement interval are determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each measurement interval.

3. The apparatus of claim 1, wherein the apparatus is a biometric monitoring device configured to be worn on a person'"'"'s body.

4. The apparatus of claim 1, wherein the apparatus is selected from the group consisting of:

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

5. The apparatus of claim 1, wherein:

the control logic to perform (b) is also configured to;

determine for each first distance measurement a first error estimate;

the control logic to perform (e) is also configured to;

convert the step count data into sequential second distance measurements based on, at least in part, application of at least one stride length to the step count data, wherein the conversion of the step count data includes a determination for each second distance measurement a second error estimate, and wherein each second distance measurement and second error estimate is associated with a temporally-corresponding one of the measurement intervals; and

apply a Kalman filter to the first distance measurements, the second distance measurements, the first error estimates, and the second error estimates in order to provide the refined distance measurements.

6. The apparatus of claim 5, wherein the at least one stride length is selected from the group consisting of:

one or more stride lengths provided by the user, one or more stride lengths calculated based on a height and weight provided by the user, one or more average stride lengths based on demographic information corresponding with the user, one or more stride lengths determined by dividing a given distance by a number of steps taken to traverse the given distance, one or more average stride lengths determined based on an average step rate during one or more of the measurement intervals, and combinations thereof.

7. The apparatus of claim 5, wherein:

each second distance measurement indicates a distance traveled by the user during the associated measurement interval based on application of the at least one stride length to the step count data.

8. The apparatus of claim 1, wherein the control logic to perform (e) is also configured to:

determine whether, for each measurement interval, the step count data indicates a step frequency that is substantially zero; and

determine the refined distance measurements by, at least in part, setting the refined distance measurements to zero for each measurement interval where the step count data indicates a step frequency that is substantially zero.

9. A method implemented in an apparatus containing a global-positioning system (GPS) receiver and at least one motion sensor, the method comprising:

(a) obtaining GPS data from the GPS receiver;

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

(c) obtaining data from the at least one motion sensor;

(d) determining step count data from the data obtained from the at least one motion sensor for each of the measurement intervals;

(e) determining a refined distance measurement for each of measurement intervals using both the first distance measurements and the step count data, wherein the refined distance measurements indicate the distance traveled by a user of the apparatus; and

(f) presenting one or more of the refined distance measurements on a display.

10. The method of claim 9, wherein the refined distance measurements for each measurement interval are determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each measurement interval.

11. The method of claim 9, wherein the GPS receiver and the at least one motion sensor are part of a biometric monitoring device designed to be worn on a person'"'"'s body.

12. The method of claim 9, wherein the GPS receiver and the at least one motion sensor are part of a device selected from the group consisting of:

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

13. The method of claim 9, wherein:

(b) comprises;

determining, for each first distance measurement a first error estimate; and

(e) comprises;

converting the step count data into sequential second distance measurements based on, at least in part, application of at least one stride length to the step count data, wherein the conversion of the step count data includes a determination for each second distance measurement a second error estimate, and wherein each second distance measurement and second error estimate is associated with a temporally-corresponding one of the measurement intervals; and

applying a Kalman filter to the first distance measurements, the second distance measurements, the first error estimates, and the second error estimates in order to provide the refined distance measurements.

14. The method of claim 13, wherein the at least one stride length is selected from the group consisting of:

one or more stride lengths provided by the user, one or more stride lengths calculated based on a height and weight provided by the user, one or more average stride lengths based on demographic information corresponding with the user, one or more stride lengths determined by dividing a given distance by a number of steps taken to traverse the given distance, one or more average stride lengths determined based on an average step rate during one or more of the measurement intervals, and combinations thereof.

15. The method of claim 13, wherein:

each second distance measurement indicates a distance traveled during the associated measurement interval based on applying the at least one stride length to the step count data.

16. The method of claim 9, wherein (e) comprises:

determining whether, for each measurement interval, the step count data indicates a step frequency that is substantially zero; and

determining the refined distance measurements by, at least in part, setting the refined distance measurements to zero for each measurement interval where the step count data indicates a step frequency that is substantially zero.

17. A non-transitory, machine-readable storage medium, wherein the storage medium stores computer-executable instructions for controlling at least one processor communicatively connected with a global positioning system (GPS) receiver and at least one motion sensor of an apparatus to:

(a) obtain GPS data from the GPS receiver,

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

(c) obtain data from the at least one motion sensor,

(d) determine step count data from the data obtained from the at least one motion sensor for each of the measurement intervals,

(e) determine a refined distance measurement for each of the measurement intervals using both the first distance measurements and the step count data, wherein the refined distance measurements indicate the distance traveled by a user of the apparatus, and

(f) present one or more of the refined distance measurements on a display.

18. The storage medium of claim 17, wherein the refined distance measurements for each measurement interval are determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each measurement interval.

19. The storage medium of claim 17, wherein the apparatus is a biometric monitoring device configured to be worn on a person'"'"'s body.

20. The storage medium of claim 17, wherein the apparatus is selected from the group consisting of:

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

21. The storage medium of claim 17, wherein:

the instructions for controlling the at least one processor to perform (b) include instructions for controlling the at least one processor to;

determine for each first distance measurement a first error estimate;

the instructions for controlling the at least one processor to perform (e) include instructions for controlling the at least one processor to;

convert the step count data into sequential second distance measurements based on, at least in part, application of at least one stride length to the step count data, wherein the conversion of the step count data includes a determination for each second distance measurement a second error estimate, and wherein each second distance measurement and second error estimate is associated with a temporally-corresponding one of the measurement intervals; and

apply a Kalman filter to the first distance measurements, the second distance measurements, the first error estimates, and the second error estimates in order to provide the refined distance measurements.

22. The storage medium of claim 21, wherein the at least one stride length is selected from the group consisting of:

one or more stride lengths provided by the user, one or more stride lengths calculated based on a height and weight provided by the user, one or more average stride lengths based on demographic information corresponding with the user, one or more stride lengths determined by dividing a given distance by a number of steps taken to traverse the given distance, one or more average stride lengths determined based on an average step rate during one or more of the measurement intervals, and combinations thereof.

23. The storage medium of claim 21, wherein:

each second distance measurement indicates a distance traveled during the associated measurement interval based on application of the at least one stride length to the step count data.

24. The storage medium of claim 17, wherein the instructions for controlling the at least one processor to perform (e) include instructions for controlling the at least one processor to:

determine whether, for each measurement interval, the step count data indicates a step frequency that is substantially zero; and

determine the refined distance measurements by, at least in part, setting the refined distance measurements to zero for each measurement interval where the step count data indicates a step frequency that is substantially zero.