LOCALIZATION ACTIVITY CLASSIFICATION SYSTEMS AND METHODS

• US 20150198443A1
• Filed: 01/10/2014
• Published: 07/16/2015
• Est. Priority Date: 01/10/2014
• Status: Abandoned Application

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent USA, Inc. (Nokia Corporation)
• Inventors: Mirowski, Piotr, Yi, Saehoon, Ho, Tin
• Application Number:
  US14/152,209
• Publication Number:
  US 20150198443A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 5/06 by using barometric means
• CPC Class Codes: G06F 18/295 Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

A system and method for providing multi-floor activity classification for a mobile device within a multi-floor environment includes an activity recognition module receiving inertial readings and pressure readings from the mobile device. The activity recognition module classifies activities for the mobile device from the inertial readings and the pressure readings.

Claims

1. A system for providing multi-floor activity classification for a mobile device within a multi-floor environment, the system comprising:

an activity recognition module receiving inertial readings from an inertial measurement unit of the mobile device and pressure readings from a barometer, the activity recognition module classifying activities for the mobile device from the inertial readings and the pressure readings.

2. The system according to claim 1, wherein the inertial readings include acceleration readings from an accelerometer and gyroscopic readings from a gyroscope.

3. The system according to claim 2, wherein the activity recognition module includes a feature extractor that extracts pose invariant features from the accelerometer and gyroscope readings, the activity classification being based on the pose invariant features.

4. The system according to claim 3, wherein the pose invariant features are represented by a vector of features from an autocorrelation matrix of the accelerometer and gyroscope readings.

5. The system according to claim 3, wherein the feature extractor extracts statistical data from the pressure readings, the activity classification being based on the statistical data.

6. The system according to claim 1, additionally comprising:

a post-processing module receiving the activity classifications from the activity recognition module and the pressure readings from a barometer of the mobile device, the post-processing module determining an activity label and floor label for the mobile device based on the activity classifications and the pressure readings.

7. The system according to claim 6, wherein the post-processing module includes a Hidden Markov Model.

8. The system according to claim 6, additionally comprising:

a geolocalization system including known locations for at least one of a staircase, an elevator or an escalator within the multi-floor environment, the geolocalization system locating the mobile device within the multi-floor environment based on the known locations and activity labels including at least one of taking stairs, elevators or escalators.

9. The system according to claim 8, wherein the geolocalization system is adapted to reconstruct a trajectory for the mobile device within the multi-floor environment based on the activity label, the floor label and position estimates from the inertial measurement unit of the mobile device.

10. The system according to claim 6, additionally comprising:

a geolocalization system including pre-defined activities indicative of known locations within the multi-floor environment, the geolocalization system locating the mobile device within the multi-floor environment based on the known locations when the pre-defined activities are detected from the activity labels.

11. A computerized localization method comprising:

receiving, at an activity recognition module executing on a processor, inertial readings from an inertial measurement unit of a mobile device and pressure readings from a barometer; and

classifying, by the activity recognition module executing on the processor, activities for the mobile device from the inertial readings and the pressure readings.

12. The computerized method according to claim 11, wherein the inertial readings include acceleration readings from an accelerometer and gyroscopic readings from a gyroscope.

13. The computerized method according to claim 12, additionally comprising:

extracting, by a feature extractor of the activity recognition module, pose invariant features from the accelerometer and gyroscope readings; and

classifying the activities based on the pose invariant features.

14. The computerized method according to claim 13, wherein extracting the pose invariant features includes determining a vector of features from an autocorrelation matrix of the accelerometer and gyroscope readings.

15. The computerized method according to claim 13, additionally comprising:

extracting, by the feature extractor of the activity recognition module, statistical data from the pressure readings; and

classifying the activities based on the statistical data.

16. The computerized method according to claim 11, additionally comprising:

post-processing, by a post-processing module executing on the processor, the activity classifications from the activity recognition module and the pressure readings from the barometer in a Hidden Markov Model to determine an activity label and a floor label for the mobile device based on the activity classifications and the pressure readings.

17. A non-transitory, tangible computer-readable medium storing instructions adapted to be executed by at least one processor of a mobile device to perform a method comprising the steps of:

receiving, at an activity recognition module executing on the at least one processor, inertial readings from an inertial measurement unit of a mobile device and pressure readings from a barometer; and

classifying, by the activity recognition module executing on the at least one processor, activities for the mobile device from the inertial readings and the pressure readings.

18. The non-transitory, tangible computer-readable medium of claim 17, additionally storing instructions adapted to be executed by the at least one processor to perform the steps of:

extracting, by a feature extractor of the activity recognition module, pose invariant features from the inertial readings; and

classifying the activities based on the pose invariant features.

19. The non-transitory, tangible computer-readable medium of claim 18, additionally storing instructions adapted to be executed by the at least one processor to perform the step of:

extracting, by the feature extractor of the activity recognition module, statistical data from the pressure readings; and

classifying the activities based on the statistical data.

20. The non-transitory, tangible computer-readable medium of claim 17, additionally storing instructions adapted to be executed by the at least one processor to perform the step of:

post-processing, by a post-processing module executing on the at least one processor, the activity classifications from the activity recognition module and the pressure readings from the barometer in a Hidden Markov Model to determine an activity label and a floor label for the mobile device based on the activity classifications and the pressure readings.