Human activity monitoring device with distance calculation

• US 7,647,196 B2
• Filed: 08/08/2007
• Issued: 01/12/2010
• Est. Priority Date: 08/08/2007
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: DP Technologies, Inc.
• Inventors: Kahn, Philippe, Christensen, Mark Andrew, Vogel, David, Kinsolving, Arthur, Mietz Egli, Paul Andrew, Lee, Brian Y.
• Primary Examiner(s): Feliciano, Eliseo Ramos
• Assistant Examiner(s): Suarez, Felix E
• Application Number:
  US11/891,112
• Publication Number:
  US 20090043531A1
• Time in Patent Office: 888 Days
• Field of Search: 702/127, 702/141, 702/150, 702/160, 702/173, 702/178, 735/10, 340/573.1, 705/2
• US Class Current: 702/149
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• 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: A63B 2071/063 Spoken or verbal instructions
• CPC Class Codes: A63B 2071/0647 Visualisation of executed movements
• CPC Class Codes: A63B 2071/0655 Tactile feedback
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/18 Inclination, slope or curvature
• CPC Class Codes: A63B 2220/20 Distances or displacements
• CPC Class Codes: A63B 2220/22 Stride length
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2220/70 Measuring or simulating ambient conditions, e.g. weather, terrain or surface conditions
• CPC Class Codes: A63B 2220/72 Temperature
• CPC Class Codes: A63B 2220/74 Atmospheric pressure
• CPC Class Codes: A63B 2220/75 Humidity
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/02 Testing, calibrating or measuring of equipment
• CPC Class Codes: A63B 2225/20 : with means for remote communication, e.g. internet or the like
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 2230/75 : calorie expenditure
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0084 : Exercising apparatus with means for competitions, e.g. virtual races
• CPC Class Codes: A63B 69/0028 : for running, jogging or speed-walking movable endless bands A63B22/02
• CPC Class Codes: G01C 21/16 : by integrating acceleration or speed, i.e. inertial navigation
• CPC Class Codes: G01C 22/006 : Pedometers
• CPC Class Codes: G01S 19/19 : Sporting applications
• CPC Class Codes: G01S 2205/08 : Sport
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

A method of monitoring human activity includes monitoring accelerations using an inertial sensor disposed at one of a plurality of locations on a human body, wherein at least one of the plurality of locations is not a foot location. A plurality of steps are counted based on the accelerations. A gait characteristic of the plurality of steps is determined. The characteristic is used to determine a stride length. At least one of a distance traveled and a speed of travel are determined based on the stride length.

Claims

1. A method of monitoring human activity by a mobile device, comprising:

monitoring accelerations using an inertial sensor disposed at one of a plurality of locations on a human body, wherein at least one of the plurality of locations is not a foot location;

counting a plurality of steps based on the accelerations;

determining a gait characteristic of the plurality of steps;

determining one or more user attributes based at least in part on historical data collected by the inertial sensor;

using a stride length to gait characteristic correlation to determine a stride length from the gait characteristic, wherein the correlation is based at least in part on the determined one or more user attributes; and

determining at least one of a distance traveled and a speed of travel based on the stride length.

2. The method of claim 1, wherein the gait characteristic comprises a step cadence.

3. The method of claim 1, wherein determining the stride length includes locating a stride length associated with the gait characteristic in a data structure.

4. The method of claim 3, wherein the data structure includes a plurality of entries, each of the plurality of entries associating a distinct stride length with one or more distinct gait characteristics, further comprising:

modifying the data structure based on the one or more user attributes to calibrate the stride length by changing one or more of the plurality of entries.

5. The method of claim 3, further comprising:

receiving a user input of one or more user attributes; and

generating the data structure using the one or more user attributes.

6. The method of claim 1, further comprising:

receiving distance information; and

automatically calibrating the stride length based on the received distance information.

7. The method of claim 6, wherein the distance information is based on at least one of global positioning system (GPS) data, network triangulation data, or user input, the method further comprising:

adjusting the stride length based on a difference between the received distance information and the determined distance traveled.

8. The method of claim 1, further comprising:

receiving a heart rate from a heart rate sensor; and

determining information about the distance traveled based on the heart rate.

9. The method of claim 8, wherein determining information comprises determining an incline, and adjusting the stride length to gait characteristic correlation based on the incline using an incline adjustment factor.

10. The method of claim 1, further comprising:

comparing the distance traveled and the speed of travel to stored race data to generate a comparison result; and

presenting a real time performance indication that includes the comparison result.

11. The method of claim 10, further comprising:

receiving the stored race data from one of a server and a mobile device.

12. The method of claim 10, further comprising:

normalizing at least one of the distance traveled, the speed of travel, the stored distance traveled, and the stored speed of travel before comparing.

13. A mobile apparatus comprising:

an inertial sensor to monitor accelerations from one of a plurality of locations on a body, wherein at least one of the plurality of locations is not a foot location;

a step counting logic coupled with the inertial sensor to count a plurality of steps based on the accelerations;

a gait logic coupled with the step counting logic to determine a gait characteristic of the plurality of steps;

an attribute logic to determine one or more user attributes based at least in part on historical data collected by the inertial sensor; and

a distance logic coupled with the gait logic to determine a stride length of the plurality of steps based on the gait characteristic and the one or more user attributes, and to apply the stride length to the plurality of steps to determine at least one of a distance traveled and a speed of travel.

14. The mobile apparatus of claim 13, further comprising:

a calibration logic connected with the distance logic, the calibration logic to receive distance information and to automatically calibrate the stride lengths based on the received distance information.

15. The mobile apparatus of claim 13, further comprising:

the attribute logic to calibrate the stride length according to the one or more user attributes.

16. The mobile apparatus of claim 13, further comprising:

the distance logic to determine information about the distance traveled based on a heart rate of the user.

17. The mobile apparatus of claim 16, wherein determining information comprises determining an incline, the apparatus further comprising:

the distance logic to adjust a stride length to gait characteristic correlation based on the incline using an incline adjustment factor.

18. The mobile apparatus of claim 13, further comprising:

a competition logic to compare the distance traveled and the speed of travel to stored race data to generate a comparison result, and to present a real time performance indication that includes the comparison result.

19. The mobile apparatus of claim 13, further comprising:

a competition logic to enable users to set up time shifted races.

20. A machine-accessible storage medium including instructions that, when executed by a machine, cause the machine to perform a method comprising:

monitoring accelerations using an inertial sensor disposed at one of a plurality of locations on a human body, wherein at least one of the plurality of locations is not a foot location;

counting a plurality of steps based on the accelerations;

determining a gait characteristic of the plurality of steps;

determining one or more user attributes based at least in part on historical data collected by the inertial sensor;

using a stride length to gait characteristic correlation to determine a stride length from the gait characteristic, wherein the correlation is based at least in part on the determined one or more user attributes; and

determining at least one of a distance traveled and a speed of travel based on the stride length.

21. The machine-accessible storage medium of claim 20, wherein the gait characteristic comprises a step cadence.

22. The machine-accessible storage medium of claim 20, wherein determining the stride length includes locating a stride length associated with the gait characteristic in a data structure.

23. The machine-accessible storage medium of claim 22, wherein the data structure includes a plurality of entries, each of the plurality of entries associating a distinct stride length with one or more distinct gait characteristics, further comprising:

modifying the data structure based on the one or more user attributes to calibrate the stride length by changing one or more of the plurality of entries.

24. The machine-accessible storage medium of claim 22, the method further comprising:

receiving a user input of one or more user attributes; and

generating the data structure using the one or more user attributes.

25. The machine-accessible storage medium of claim 20, the method further comprising:

receiving distance information; and

automatically calibrating the stride length based on the received distance information.

26. The machine-accessible storage medium of claim 25, wherein the distance information is based on at least one of:

global positioning system (GPS) data, network triangulation data, or user input, the method further comprising;

adjusting the stride length based on a difference between the received distance information and the determined distance traveled.

27. The machine-accessible storage medium of claim 20, the method further comprising:

receiving a heart rate from a heart rate sensor; and

determining information about the distance traveled based on the heart rate.

28. The machine-accessible storage medium of claim 27, wherein determining information comprises determining an incline, and adjusting the stride length to gait characteristic correlation based on the incline using an incline adjustment factor.

29. The machine-accessible storage medium of claim 20, the method further comprising:

comparing the distance traveled and the speed of travel to stored race data to generate a comparison result; and

presenting a real time performance indication that includes the comparison result.

30. The machine-accessible storage medium of claim 29, the method further comprising:

receiving the stored race data from one of a server and a mobile device.

31. The machine-accessible storage medium of claim 29, the method further comprising:

normalizing at least one of the distance traveled, the speed of travel, the stored distance traveled, and the stored speed of travel before comparing.