GPS ACCURACY REFINEMENT USING EXTERNAL SENSORS

• US 20140316305A1
• Filed: 05/29/2014
• Published: 10/23/2014
• 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
• Granted Patent: US 9,597,014 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• 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 5/33 specially adapted for cooperation with other devices
• 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) obtain data from the at least one motion sensor,

c) determine step count data from the data obtained from the at least one motion sensor, and

d) determine first refined data for one or more first measurement intervals using both the GPS data and the step count data.

2. The apparatus of claim 1, wherein the first refined data for each first measurement interval is determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each first 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 is configured to perform (d) by:

converting the GPS data into a plurality of sequential first measurements, each first measurement having a first error estimate and each first measurement and first error estimate associated with a different first measurement interval;

converting the step count data into a plurality of sequential second measurements by, at least in part, applying at least one stride length to the step count data, each second measurement having a second error estimate and each second measurement and second error estimate associated with a temporally-corresponding one of the first measurement intervals; and

applying a Kalman filter to the one or more first measurements, the one or more second measurements, the first error estimates, and the second error estimates in order to provide the first refined data.

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 a user of the apparatus, one or more stride lengths calculated based on a height and weight provided by a user of the apparatus, one or more average stride lengths based on demographic information corresponding with a user of the apparatus, 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 the first measurement interval, and combinations thereof.

7. The apparatus of claim 5, wherein:

each first measurement is indicative of a distance traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a distance traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of distance traveled.

8. The apparatus of claim 5, wherein:

each first measurement is indicative of a speed traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a speed traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of speed.

9. The apparatus of claim 1, wherein the GPS data is speed and the control logic is configured to perform (d), in part, by:

determining the first refined data by smoothing the GPS data according to;

RS=(1−

β

)·

RS_prev+β

·

S

β

∝

variance (Step Rate)

where RS=first refined data (speed) for one of the first measurement intervals, RS_prev=the first refined data from the previous first measurement interval, and S=speed measured from the GPS data during the first measurement interval.

10. The apparatus of claim 1, wherein the control logic is configured to perform (d), in part, by:

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

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

11. A method comprising:

a) obtaining GPS data from a GPS receiver;

b) obtaining data from at least one motion sensor;

c) determining step count data from the data obtained from the at least one motion sensor; and

d) determining first refined data for one or more first measurement intervals using both the GPS data and the step count data.

12. The method of claim 11, wherein the first refined data for each first measurement interval is determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each first measurement interval.

13. The method of claim 11, 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.

14. The method of claim 11, 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.

15. The method of claim 11, wherein (d) is performed by:

converting the GPS data into a plurality of sequential first measurements, each first measurement having a first error estimate and each first measurement and first error estimate associated with a different first measurement interval;

converting the step count data into a plurality of sequential second measurements by, at least in part, applying at least one stride length to the step count data, each second measurement having a second error estimate and each second measurement and second error estimate associated with a temporally-corresponding one of the first measurement intervals; and

applying a Kalman filter to the one or more first measurements, the one or more second measurements, the first error estimates, and the second error estimates in order to provide the first refined data.

16. The method of claim 15, wherein the at least one stride length is selected from the group consisting of:

one or more stride lengths provided by a user of the apparatus, one or more stride lengths calculated based on a height and weight provided by a user of the apparatus, one or more average stride lengths based on demographic information corresponding with a user of the apparatus, 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 the first measurement interval, and combinations thereof.

17. The method of claim 15, wherein:

each first measurement is indicative of a distance traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a distance traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of distance traveled.

18. The method of claim 15, wherein:

each first measurement is indicative of a speed traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a speed traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of speed.

19. The method of claim 11, wherein the GPS data is speed and (d) is performed, in part, by:

determining the first refined data by smoothing the GPS data according to;

RS=(1−

β

)·

RS_prev+β

·

S

β

∝

variance (Step Rate)

where RS=first refined data (speed) for one of the first measurement intervals, RS_prev=the first refined data from the previous first measurement interval, and S=speed measured from the GPS data during the first measurement interval.

20. The method of claim 11, wherein (d) is performed, in part, by:

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

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

21. 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 to:

a) obtain GPS data from the GPS receiver,

b) obtain data from the at least one motion sensor,

c) determine step count data from the data obtained from the at least one motion sensor, and

d) determine first refined data for one or more first measurement intervals using both the GPS data and the step count data.

22. The storage medium of claim 21, wherein the first refined data for each first measurement interval is determined using temporally-corresponding and independently-measured GPS-based distances and step counts associated with each first measurement interval.

23. The storage medium of claim 21, wherein the apparatus is a biometric monitoring device configured to be worn on a person'"'"'s body.

24. The storage medium of claim 21, wherein the apparatus is selected from the group consisting of:

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

25. The storage medium of claim 21, wherein the storage medium further stores instructions to perform (d) by:

converting the GPS data into a plurality of sequential first measurements, each first measurement having a first error estimate and each first measurement and first error estimate associated with a different first measurement interval;

converting the step count data into a plurality of sequential second measurements by, at least in part, applying at least one stride length to the step count data, each second measurement having a second error estimate and each second measurement and second error estimate associated with a temporally-corresponding one of the first measurement intervals; and

applying a Kalman filter to the one or more first measurements, the one or more second measurements, the first error estimates, and the second error estimates in order to provide the first refined data.

26. The storage medium of claim 25, wherein the at least one stride length is selected from the group consisting of:

one or more stride lengths provided by a user of the apparatus, one or more stride lengths calculated based on a height and weight provided by a user of the apparatus, one or more average stride lengths based on demographic information corresponding with a user of the apparatus, 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 the first measurement interval, and combinations thereof.

27. The storage medium of claim 25, wherein:

each first measurement is indicative of a distance traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a distance traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of distance traveled.

28. The storage medium of claim 25, wherein:

each first measurement is indicative of a speed traveled during the associated first measurement interval based on the GPS data;

each second measurement is indicative of a speed traveled during the associated first measurement interval based on applying the at least one stride length to the step count data; and

the first refined data is indicative of speed.

29. The storage medium of claim 21, wherein the GPS data is speed and the storage medium further stores instructions to perform (d), in part, by:

determining the first refined data by smoothing the GPS data according to;

RS=(1−

β

)·

RS_prev+β

·

S

β

∝

variance (Step Rate)

where RS=first refined data (speed) for one of the first measurement intervals, RS_prev=the first refined data from the previous first measurement interval, and S=speed measured from the GPS data during the first measurement interval.

30. The storage medium of claim 21, wherein the storage medium further stores instructions to perform (d), in part, by:

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

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