TECHNIQUES FOR IMPROVED PEDOMETER READINGS

• US 20130085677A1
• Filed: 09/30/2011
• Published: 04/04/2013
• Est. Priority Date: 09/30/2011
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Gupta, Saurabh, Dixit, Vinay Bethgiri Ganesh, Modi, Yash Rohit
• Application Number:
  US13/251,128
• Publication Number:
  US 20130085677A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 2560/0431 Portable apparatus, e.g. comprising a handle or case
• 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 5/7225 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: G01C 22/006 Pedometers
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration

Abstract

Techniques are provided for improving pedometer readings. In some embodiments, motion data, such as acceleration data is detected, and a magnitude of the acceleration data, referred to as the modulus, is processed. The modulus is used to detect walking steps and running steps during a measurement. In some embodiments, a distance ran is calculated based on the detected running steps and an estimated running stride length, and a distance walked is calculated based on the detected walking steps and an estimated walking stride length. The estimated running and walking stride lengths are calculated based on various parameters associated with the acceleration data, population data, and user-specific data. Expended calories may be estimated based on the distance ran and walked. The distance analysis process further includes calibration techniques, including, for example, least squares simple regression, least squares multiple regression, and K-factor analysis.

Claims

1. A method of estimating traveled distance using an electronic device, the method comprising:

determining a first estimation of distance traveled based on a number of detected steps, an estimated walking stride length, and an estimated running stride length, wherein the number of detected steps comprises a number of walking steps, a number of running steps, or both, and wherein the estimated running stride length and the estimated walking stride length are computed based on one or more parameters.

2. The method of claim 1, wherein determining the first estimation of distance traveled comprises:

calculating a distance ran by multiplying the number of running steps by the estimated running stride;

calculating the distance walked by multiplying the number of walking steps by the estimated walking stride; and

adding the distance ran with the distance walked to produce the distance traveled.

3. The method of claim 2, displaying the distance ran, the distance walked, or the distance traveled, or a combination thereof.

4. The method of claim 3, wherein displaying the distance ran, the distance walked, or the distance traveled, or a combination thereof comprises updating the displayed distances substantially in real-time.

5. The method of claim 2, comprising:

calculating calories expended based on the distance ran, the distance walked, or the distance traveled, or a combination thereof; and

displaying the calculated calories expended.

6. The method of claim 5, wherein displaying the calculated calories expended comprises updating the displayed calories expended substantially in real-time.

7. The method of claim 1, wherein determining the first estimation of distance traveled comprises calculating a running stride length and the estimated walking stride length based on one or more parameters comprising frequency domain parameters of acceleration data detected by the electronic device or time domain parameters of the acceleration data, user-specific parameters of a user using the electronic device, or combinations thereof.

8. The method of claim 1, wherein determining the first estimation of distance traveled comprises calculating a running stride length and the estimated walking stride length based on parameters of acceleration data detected by the electronic device, wherein the acceleration data comprises fourth root of acceleration difference, root mean square (RMS) acceleration, step frequency, mean absolute differential value (MADV), cube root of velocity, the difference between acceleration variance and energy, or combinations thereof.

9. The method of claim 1, wherein determining the first estimation of distance traveled comprises calculating a running stride length and the estimated walking stride length based on user-specific parameters comprising height, gender, weight, or combinations thereof.

10. The method of claim 9, comprising receiving user-specific parameters from a user.

11. The method of claim 1, comprising:

receiving an actual distance input at the electronic device; and

determining a second estimation of distance traveled based on a comparison between the actual distance and the first estimation of distance traveled.

12. The method of claim 1, comprising:

receiving an actual distance input at the electronic device;

calibrating the electronic device based on the first estimation of distance traveled and the actual distance.

13. The method of claim 12, comprising determining a second estimation of distance traveled based on the calibration.

14. The method of claim 12, wherein calibrating the electronic device comprises using a least squares simple regression, a least squares multiple regression, a K-factor analysis, or combinations thereof.

15. An electronic device comprising:

a processor configured to;

receive acceleration data from an accelerometer of the electronic device;

process the acceleration data;

calculate a distance ran based on counted running steps, an estimated running stride length, and parameters of the acceleration data; and

calculate a distance walked based on counted walking steps, an estimated running stride length, and parameters of the acceleration data; and

a display configured to display the distance ran, the distance walked, a total distance combining the distance walked and the distance run, or combinations thereof.

16. The electronic device of claim 15, wherein the processor is configured to calculate calories expended based on the distance ran and the distance walked, and wherein the display is configured to display the calories expended.

17. The electronic device of claim 15, comprising memory suitable for storing algorithms associated with calculating the distance ran and the distance walked.

18. The electronic device of claim 15, comprising memory configured to store the parameters of the acceleration data.

19. The electronic device of claim 15, comprising memory, wherein the estimated running stride length and the estimated walking stride length are based on population data, and wherein the memory is configured to store the population data and algorithms associated with estimating the running stride length and the walking stride length.

20. The electronic device of claim 15, comprising a user interface configured to receive user-specific parameters entered by a user of the electronic device, wherein the estimated running stride length and the estimated walking stride length are calculated from the user-specific parameters.

21. The electronic device of claim 15, comprising a user interface configured to receive an actual distance traveled, wherein the processor is configured to calibrate the electronic device based on the total distance estimated and the actual distance traveled.

22. The electronic device of claim 15, wherein the processor is configured to calibrate the distance ran calculation and the distance walked calculation.

23. The electronic device of claim 22, wherein the processor is configured to calibrate the distance ran calculation and the distance walked calculation using least squares simple regression, a least squares multiple regression, a K-factor analysis, or combinations thereof.

24. A method comprising:

estimating a distance traveled on an electronic device based on a number of steps detected by the electronic device and an estimated stride length, wherein the estimated stride length is computed based on one or more parameters;

receiving an input on the electronic device of an actual distance traveled; and

calibrating the electronic device based on the estimated distance traveled and the actual distance traveled.

25. The method of claim 24, wherein estimating the distance traveled comprises calculating the estimated stride length based on two population data points, and wherein calibrating the electronic device comprises:

calculating an actual stride length based on the actual distance traveled and a number of steps detected, wherein the calculated actual stride length represents a new data point;

adding the new data point to a data point set comprising the two population data points; and

determining a calibrated distance based on a linear fit on the data point set.

26. The method of claim 25, comprising weighting the new data point to twice the weight of each of the two population data points.

27. The method of claim 25, wherein estimating the distance traveled comprises calculating the estimated stride length based on a 15^th percentile population data point and a 85^th percentile population data point.

28. The method of claim 24, wherein estimating the distance traveled comprises calculating the estimated stride length based on 50 population data points, and wherein calibrating the electronic device comprises:

calculating an actual stride length based on the actual distance traveled and a number of steps detected, wherein the calculated actual stride length represents a new data point;

weighting the new data point to 50 data points;

adding the weighted data point to a data point set comprising the 50 population data points; and

determining a calibrated distance based on a linear fit on the data point set.

29. The method of claim 24, wherein calibrating the electronic device comprises:

calculating a new K-factor based on a comparison between the actual distance traveled and the estimated distance traveled;

adding the new K-factor to a circular buffer;

averaging values in the circular buffer to produce a current K factor; and

using the current K-factor to estimate a new distance traveled.