Sensor fusion for activity identification

• US 9,144,398 B1
• Filed: 07/22/2014
• Issued: 09/29/2015
• Est. Priority Date: 08/29/2008
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: DP Technologies, Inc.
• Inventors: Kahn, Philippe Richard, Kinsolving, Arthur, Christensen, Mark Andrew, Lee, Brian Y., Vogel, David
• Primary Examiner(s): RICHMAN, GLENN E
• Application Number:
  US14/338,339
• Time in Patent Office: 434 Days
• Field of Search: 482 1- 9, 482900-902, 340/573.1, 600/300
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G01S 19/49 whereby the further system is an inertial position system, e.g. loosely-coupled
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: Y10S 482/901 having computer circuitry

Abstract

An mobile device to monitor human activity using multiple sensors comprising a processing unit to provide a first estimation user activity data based on data from a first sensor, the first estimation having a first accuracy estimation, and to provide a second estimation of user activity data based on data from a second sensor, the second estimation having a second accuracy estimation, and the processor to calculate the user activity statistic based on the first estimation and the second estimation, wherein the calculated user activity statistic is a result of a weighted combination of the first estimation and the second estimation.

Claims

1. An mobile device to monitor human activity using multiple sensors comprising:

a processing unit to provide a first estimation user activity data based on data from a first sensor, the first estimation having a first accuracy estimation, and to provide a second estimation of user activity data based on data from a second sensor, the second estimation having a second accuracy estimation;

the processor to calculate the user activity statistic based on the first estimation and the second estimation, wherein the calculated user activity statistic is a result of a weighted combination of the first estimation and the second estimation.

2. The mobile device of claim 1, comprising:

the processing unit comprising a low power processor; and

a central processor, the central processor to provide other functions.

3. The mobile device of claim 1, further comprising:

the plurality of sensors including a location sensor and a motion sensor; and

the calculating logic to verify the calculated user activity based on location information.

4. The mobile device of claim 1, further comprising:

a transmission system to transmit at least a subset of the data from the plurality of sensors to an external computing device, to generate the user activity statistic.

5. The mobile device of claim 2, wherein the externally calculated user activity statistic includes a reconstruction of an event that started when the user activity was initially detected and ended when the user activity ceased to be detected.

6. The mobile device of claim 1, wherein the user activity statistic is one of a distance traveled, a route traveled, a speed of travel, a current position or a periodic human motion count.

7. The mobile device of claim 1, wherein the first sensor and the second sensor are each one of:

an inertial sensor and a location sensor.

8. The mobile device of claim 7, further comprising:

the calculating logic to verify the calculated user activity based on location information from the location sensor.

9. The mobile device of claim 7, further comprising:

the calculating logic to verify location information based on accelerations from the inertial sensor and past location information from the location sensor.

10. The mobile device of claim 1, further comprising:

a central processing unit to receive location information from the location sensor, and when the location information indicates that a user is at a particular location, the central processing unit to automatically perform a preconfigured action.

11. The mobile device of claim 10, wherein the preconfigured action comprises one of:

starting or ending a sports training application, starting or ending a run application, providing coaching, logging a workout, initiating another application, and altering an application setting.

12. The mobile device of claim 1, further comprising:

a microcontroller to receive a starting and an ending point and generate a plurality of possible paths, and further to apply defined constraints on the path, and display the possible paths to the user for selection.

13. The mobile device of claim 12, wherein if the user selects a path that does not meet a defined constraint, the defined constrain is lowered in importance.

14. The mobile device of claim 1, further comprising:

the processor to identify a path segment suitable for calibration of one of the sensors, and comparing path length for the path segment as measured by the first sensor and the second sensor, and configuring the sensor with a lower accuracy estimation.

15. An electronic device comprising:

a first sensor to receive a first set data;

a second sensor to receive a second set data;

a processor to calculate a first estimation of user activity data based on the first set of data, and a second estimation of the user activity data based on the second set of data;

the processor to calculate a user activity statistic based on the first estimation and the second estimation, wherein the calculated user activity statistic is a result of a weighted combination of the first estimation and the second estimation.

16. The electronic device of claim 15, wherein the electronic device is one of:

a cellular phone, wrist watch, mp3 player, personal digital assistant (PDA), mobile game console, a tablet, and a laptop computer.

17. The electronic device of claim 15, further comprising:

the first sensor and the second sensor selected from among;

an inertial sensor and a location sensor; and

the first estimation and the second estimation each having an accuracy estimation, wherein the weighted combination utilizes the accuracy estimations.

18. The electronic device of claim 15, further comprising:

the processor to receive a starting and an ending point and generate a plurality of possible paths, and further to apply defined constraints on the path, and display the possible paths to the user for selection.

19. The electronic device of claim 18, wherein if the user selects a path that does not meet a defined constraint, the defined constrain is lowered in importance.

20. The electronic device of claim 15, further comprising:

the processor to identify a path segment suitable for calibration of one of the sensors, and comparing path length for the path segment as measured by the first sensor and the second sensor, and configuring the sensor with a lower accuracy estimation.