Internal Sensor Based Personalized Pedestrian Location

• US 20140309964A1
• Filed: 04/11/2013
• Published: 10/16/2014
• Est. Priority Date: 04/11/2013
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Li, Fan, Zhao, Chunshui, Zhao, Feng
• Application Number:
  US13/860,929
• Publication Number:
  US 20140309964A1
• Time in Patent Office: Days
• US Class Current: 702/160
• CPC Class Codes: G01C 21/183 Compensation of inertial measurements, e.g. for temperature effects
• CPC Class Codes: G01C 22/006 Pedometers

Abstract

Step detection and step length estimation techniques may include detecting salient points in sensor data of one or more sensors. A step frequency may be used to estimate the length of a step according to a step length estimation model. The step length estimation model may be adjusted based at least in part on landmark data to better estimate a step length of the user. Additionally or alternatively, an adjusted step length estimation model may be readjusted over time to account for changes in a user, conditions, or both.

Claims

1. A method of estimating a step length, comprising:

under control of one or more processors configured with executable instructions;

receiving, from one or more sensors, one or more databases, or a combination thereof, sensor data comprising accelerometer data and landmark data;

detecting a step based at least in part on the accelerometer data;

estimating a step length based at least in part on the accelerometer data; and

adjusting the estimated step length based at least in part on the accelerometer data and the landmark data.

2. The method of claim 1, wherein the detecting the step comprises:

processing the accelerometer data; and

evaluating the processed accelerometer data to detect a cycle.

3. The method of claim 2, wherein the evaluating the processed accelerometer data comprises detecting salient points in the processed accelerometer data.

4. The method of claim 2, wherein the processing the accelerometer data comprises:

applying one or more low pass filters;

applying one or more heuristic constraints; and

applying one or more dynamic time warping operations to compare wave forms.

5. The method of claim 1, wherein the estimating the step length comprises:

estimating a step frequency from the sensor data; and

calculating the step length of the step based at least in part on the step frequency and a predetermined function of the sensor data.

6. The method of claim 1, wherein the estimating the step length comprises:

processing the accelerometer data; and

evaluating the processed accelerometer data to detect frequency of a cycle.

7. The method of claim 6, wherein the evaluating the processed accelerometer data to detect frequency of the cycle comprises applying a frequency model, wherein the frequency model is based at least in part on a linear combination of a first frequency of a first step candidate at a first time and a second frequency of a second step candidate at a second time.

8. The method of claim 1, wherein the adjusting the estimated step length comprises:

determining a true distance between a first landmark and a second landmark;

determining an estimated distance between the first landmark and the second landmark based in part on an aggregation of estimated step lengths over a traversal between the first landmark and the second landmark;

adjusting the estimated step length based at least in part on a difference between the estimated distance and the true distance.

9. The method of claim 1, wherein the estimating a step length comprises:

processing the accelerometer data;

evaluating the processed accelerometer data to detect frequency of a cycle;

applying a step length estimation model.

10. The method of claim 9, wherein the adjusting the estimated step length comprises:

determining a true distance between a first landmark and a second landmark based at least in part on the landmark data;

determining an estimated distance between the first landmark and the second landmark based in part on an aggregation of estimated step lengths over a traversal between the first landmark and the second landmark;

determining a distance between the estimated distance and the true distance; and

adjusting the step length estimation model based at least in part on the difference between the estimated distance and the true distance.

11. The method of claim 10, wherein the landmark data comprises location information of a landmark and a confidence level associated with the landmark data.

12. The method of claim 11, wherein a landmark comprises one or more of a physical structure, an intersection, a communication tower, a near field communication reader, or a peak value of a received signal strength.

13. The method of claim 10, further comprising readjusting the step length estimation model based at least in part on a difference between the estimated distance and the true distance.

14. The method of claim 13, wherein the readjusting is initiated in response to a detected error value between a determined true distance and a determined estimated distance that exceeds a predetermined threshold value.

15. The method of claim 14, further comprising determining the error value in response to one or more of a passage of a predetermined time since a previous error value determination or new landmark data is received.

16. One or more computer-readable media configured with computer-executable instructions that, when executed by one or more processors, configure the one or more processors to perform acts comprising:

receiving, from one or more sensors, one or more databases, or a combination thereof, sensor data comprising accelerometer data and landmark data;

detecting a step based at least in part on the accelerometer data;

estimating a step length based at least in part on the accelerometer data and a step length estimation model;

adjusting the step length estimation model based at least in part on the accelerometer data and the landmark data; and

determining an adjusted estimated step length.

17. The one or more computer-readable media of claim 16, wherein the landmark data comprises location information for a first landmark and a second landmark, and wherein the adjusting the step length estimation model comprises:

determining a true distance between the first landmark and the second landmark based at least in part on the landmark data;

determining an estimated distance between the first landmark and the second landmark based in part on an aggregation of estimated step lengths over a traversal between the first landmark and the second landmark;

determining a distance between the estimated distance and the true distance; and

adjusting the step length estimation model based at least in part on the difference between the estimated distance and the true distance.

18. The one or more computer-readable media of claim 17, further comprising:

determining a difference between a determined true distance and a determined estimated distance in response to a trigger, wherein the trigger comprises one or more of a lapse of a time period since a preceding determination of the difference or new landmark data is received; and

readjusting the step length estimation model based at least in part on the difference between the true distance and the estimated distance when the difference exceeds a threshold difference.

19. A system comprising:

one or more processors;

memory, communicatively coupled to the one or more processors, storing instructions that, when executed by the one or more processors, configure the one or more processors to perform acts comprising;

receiving sensor data comprising accelerometer data and landmark data from one or more sensors;

detecting a step based at least in part on the sensor data;

estimating a step length based at least in part on the sensor data and a step length estimation model;

adjusting the step length estimation model based at least in part on the sensor data comprising;

determining a true distance between the first landmark and the second landmark based at least in part on the landmark data;

determining an estimated distance between the first landmark and the second landmark based in part on an aggregation of estimated step lengths over a traversal between the first landmark and the second landmark;

determining a distance between the estimated distance and the true distance; and

adjusting the step length estimation model based at least in part on the difference between the estimated distance and the true distance; and

determining an adjusted estimated step length.

20. The one or more computer-readable media of claim 19, further comprising:

determining a difference between a determined true distance and a determined estimated distance in response to a trigger, wherein the trigger comprises one or more of a lapse of a time period since a preceding determination of the difference or new landmark data is received; and

readjusting the step length estimation model based at least in part on the difference between the true distance and the estimated distance when the difference exceeds a threshold difference.