Apparatus and method for classification of physical orientation

• US 20090171615A1
• Filed: 12/31/2007
• Published: 07/02/2009
• Est. Priority Date: 12/31/2007
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Gajjala, Anita, Shahabdeen, Junaith Ahemed
• Granted Patent: US 7,684,954 B2
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01C 21/166 Mechanical, construction or arrangement details of inertial navigation systems
• CPC Class Codes: G01C 9/00 Measuring inclination, e.g. by clinometers, by levels
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A state classifier uses learning obtained from a plurality of training algorithms each adapted to differentiate between states of physical orientation of an object in response to input data from an tri-axial accelerometer. At least two of the training algorithms are trained using data from an accelerometer mounted at a non-ideal angle. The classifier is trained to distinguish between the desired states from data collected from an tri-axial accelerometer device mounted at a plurality of respective angles with respect to a optimal axis on the object, wherein the angles are in the range of −180 degrees to +180 degrees. The classifier may include a plurality of classifiers and a decision fusion module used to combine the decisions from the respective classifiers to ascertain a state.

Claims

1. A method comprising:

a classifier using learning obtained from a plurality of training algorithms each adapted to differentiate between states of physical orientation of an object in response to input data from an tri-axial accelerometer wherein at least two of the training algorithms are trained using data from an accelerometer mounted at a non-ideal angle; and

wherein the classifier is trained to distinguish between the desired states from data collected from an tri-axial accelerometer device mounted at a plurality of respective angles with respect to a optimal axis on the object, wherein the angles are in the range of −

180 degrees to +180 degrees.

3. A method according to claim 1 wherein the classifier includes a plurality of classifiers each adapted to differentiate between states of an object under different conditions of the mount angle of the accelerometer.

4. A method according to claim 3 further including using decision fusion to predict a state of the object from classification information received each of the classifiers.

5. A method according to claim 1 wherein the non-ideal angle is in the range of −

180 degrees to +180 degrees.

6. A method according to claim 4 wherein the decision fusion combines the output from each of the classifiers for the prediction.

7. A method according to claim 6 wherein confusion in a decision for each classifier is considered to calculate a confidence level for at least one of the possible states.

8. A method according to claim 7 wherein the decision fusion produces an output vector including a confidence level for all the states after combining the decision from the individual classifiers.

2. A method according to claim 2 wherein the desired states are sedentary states and the object is a human subject, and the sedentary states are selected from the group:

sitting, standing and lying.

9. Apparatus comprising:

a plurality of classifiers to distinguish between desired states of physical orientation of an object using data collected from an tri-axial accelerometer device mounted on the object, wherein each of the classifiers receive the same data from the tri-axial accelerometer device and at least some of the classifiers are trained using different training data, wherein the training data correlates to at least one position of the tri-axial accelerometer that is not optimally oriented on the object; and

a decision fusion module to identify one of the desired states from classification information received each of the classifiers.

10. Apparatus according to claim 9 wherein the less than optimal angle is in the range of −

180 degrees to +180 degrees.

11. Apparatus according to claim 9 wherein the decision fusion modules combines the output from each of the classifiers for the prediction.

12. Apparatus according to claim 9 wherein confusion in a decision for each classifier is considered to calculate a confidence level for at least one of the possible states.

13. Apparatus according to claim 9 wherein the decision fusion produces an output vector including a confidence level for all the states after combining the decision from the individual classifiers.

14. Apparatus according to claim 9 wherein the states of physical orientation are states of physical orientation of a human subject.

15. Apparatus according to claim 14 wherein the states of physical orientation include sedentary states.