TECHNIQUES FOR IMPROVED PEDOMETER READINGS

• US 20130085700A1
• 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: Modi, Yash Rohit, Gupta, Saurabh, Dixit, Vinay Bethgeri Ganesh
• Application Number:
  US13/251,136
• Publication Number:
  US 20130085700A1
• Time in Patent Office: Days
• US Class Current: 702/104
• CPC Class Codes: G01C 22/006 Pedometers

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. Techniques involve performing frequency analysis on the modulus. In some embodiments, the modulus samples are counted perform frequency analysis on each complete sample set. Each complete sample set includes overlapping samples with the previous sample set. A suitable frequency analysis algorithm, such as fast Fourier transform (FFT) dominant frequency computation is performed on each sample set to determine the dominant frequency of the modulus. The dominant frequency of the modulus may be smoothed in some embodiments to remove irregularities such as spikes or periods of inactivity. In some embodiments, results of frequency analysis may be used to determine whether a detected step is a walking step or a running step.

Claims

1. A method for analyzing motion data, the method comprising:

calculating a dominant frequency from a current sample set of the motion data, wherein the current sample set comprises a first sample portion and a second sample portion, wherein the first sample portion comprises samples previously analyzed in an immediately preceding sample set from the current sample set.

2. The method of claim 1, comprising determining that a current sample set is complete before calculating the dominant frequency from the current sample set.

3. The method of claim 2, wherein determining that the current sample set is complete comprises determining that the current sample set comprises 1024 samples of the motion data.

4. The method of claim 2, wherein determining that the current sample set is complete comprises determining that the current sample set comprises 512 samples in the first sample portion and 512 samples in the second sample portion.

5. The method of claim 1, wherein calculating the dominant frequency comprises performing a fast Fourier transform (FFT) dominant frequency computation on the motion data.

6. The method of claim 1, wherein the motion data comprises a modulus signal comprising a magnitude of acceleration data, wherein the acceleration data is detected by a motion sensor of an electronic device.

7. The method of claim 6, wherein the dominant frequency comprises a transform of the modulus signal.

8. The method of claim 6, comprising calculating an average acceleration magnitude of the modulus signal in the current sample set, resulting in a moving average.

9. The method of claim 8, comprising transmitting the moving average to a threshold filter configured to filter the modulus signal based on the moving average.

10. The method of claim 1, comprising transmitting the dominant frequency to a frequency filter configured to filter the modulus signal based on the dominant frequency.

11. The method of claim 1, wherein the dominant frequency comprises an estimate of a motion of an electronic device from which the motion data is detected.

12. The method of claim 1, comprising smoothing the dominant frequency to reject irregularities from the calculated dominant frequency.

13. The method of claim 1, comprising detecting a step based on the dominant frequency calculation and determining whether the detected step is a running step or a walking step based on the dominant frequency calculation.

14. The method of claim 13, wherein determining whether the detected step is a running step or a walking step is based on one or more parameters of the motion data, wherein the parameters comprise root mean square acceleration (RMS), mean absolute differential value (MADV), acceleration variance, cube root of velocity, fourth root of acceleration difference, step frequency, signal energy, signal entropy, frequency, the multiple of frequency and entropy, the difference between acceleration variance and energy, or combinations thereof.

15. An electronic device comprising one or more processors configured to:

receive a motion signal comprising a magnitude of acceleration data detected by the electronic device;

calculate a dominant frequency of a previous sample set of the motion signal, wherein the previous sample set comprises a first sample portion and a second sample portion; and

calculate a dominant frequency of a current sample set of the motion signal, wherein the current sample set comprises the second sample portion and a third sample portion.

16. The electronic device of claim 15, comprising a motion sensor configured to detect the acceleration data, wherein the motion sensor is a three-axis accelerometer and the acceleration data comprises acceleration signals in the x-axis, y-axis, and z-axis.

17. The electronic device of claim 15, wherein the one or more processors is configured to calculate a magnitude of the acceleration data to produce the motion signal.

18. The electronic device of claim 15, wherein each of the first sample portion, second sample portion, and third sample portion comprises 512 samples.

19. The electronic device of claim 15, wherein the one or more processors is configured to calculate the dominant frequency by performing a fast Fourier transform (FFT) dominant frequency computation on the motion signal.

20. The electronic device of claim 15, wherein the one or more processors is configured to determine a total number of steps walked and a total number of steps ran based on the dominant frequency calculations.

21. The electronic device of claim 20, wherein the one or more processors comprises a machine learning model configured to analyze one or more parameters of the motion signal, wherein the one or more parameters comprise root mean square acceleration (RMS), mean absolute differential value (MADV), acceleration variance, cube root of velocity, fourth root of acceleration difference, step frequency, signal energy, signal entropy, frequency, the multiple of frequency and entropy, the difference between acceleration variance and energy, or combinations thereof.

22. The electronic device of claim 15, comprising memory configured to store algorithms and data associated with calculating the dominant frequency.

23. The electronic device of claim 15, comprising memory configured to store the calculated dominant frequency.

24. A method comprising:

calculating a dominant frequency of a motion modulus signal for each of a plurality of consecutive sample sets of the motion modulus signal, wherein the motion modulus signal comprises a magnitude of acceleration data, and wherein each sample set of the plurality of consecutive sample sets comprises samples from an immediately preceding sample set.

25. The method of claim 24, wherein each sample set overlaps with the immediately preceding sample set by half of the samples in each of the plurality of consecutive sample sets.

26. The method of claim 24, wherein each of the plurality of consecutive sample sets comprises 1024 samples.

27. The method of claim 24, comprising processing the calculated dominant frequency to remove irregularities of the dominant frequencies.

28. The method of claim 24, wherein calculating the dominant frequency of the motion modulus signal comprises performing a fast Fourier transform (FFT) dominant frequency computation on the motion modulus signal.

29. The method of claim 24, comprising detecting inactive periods based on the motion modulus signal and discarding samples associated with the inactive periods from dominant frequency calculation.