Posture state classification for a medical device

Patent metadata

Field	Value
Publication number	US-9545518-B2
Application number	US-43299309-A
Country	US
Kind code	B2
Filing date	Apr 30, 2009
Priority date	Jul 11, 2008
Publication date	Jan 17, 2017
Grant date	Jan 17, 2017

Abstract

Techniques for posture classification of a patient in a coordinate system of a sensor. According to one aspect, a defined vector is obtained from a sensor disposed in a substantially fixed manner relative to the patient. The defined vector is described in a coordinate system of the sensor and without regard to an orientation in which the sensor is disposed in relation to the patient. A detected vector is obtained from the sensor that is described using the coordinate system of the sensor. The detected vector and the defined vector to are used to classify the posture state of the patient without regard to the orientation in which the sensor is disposed in relation to the patient. A response may be initiated by a medical device, which may include adjusting therapy delivery.

First claim

What is claimed is:

1. A method of classifying a posture state of a patient, comprising:
obtaining a defined vector from at least one sensor disposed in a substantially fixed manner relative to the patient, the defined vector being described in a coordinate system of the at least one sensor and without regard to an orientation in which the sensor is disposed in relation to the patient;
obtaining a detected vector from the at least one sensor, the detected vector being described in the coordinate system of the at least one sensor and being indicative of the posture state of the patient;
comparing the detected vector and the defined vector;
classifying the posture state of the patient based on the comparison and without regard to the orientation in which the sensor is disposed in relation to the patient; and
initiating via a medical device an action related to providing care for the patient, the action being based on the posture state classification.

2. The method of claim 1 , wherein obtaining the defined vector includes:
measuring outputs of the at least one sensor while the patient assumes a defined posture state; and
associating the measured outputs with the defined vector.

3. The method of claim 1 , further comprising associating a region in space with the defined vector, and wherein comparing the detected vector and the defined vector further comprises determining whether the detected vector lies within the region in space.

4. The method of claim 3 , further comprising:
obtaining multiple defined vectors, each being associated with a respective region in space; and
wherein comparing the detected vector and the defined vector comprises determining whether the detected vector lies within any of the regions in space associated with the multiple defined vectors.

5. The method of claim 1 , further comprising:
associating a tolerance with the defined vector; and
determining a similarity between the defined vector and the detected vector; and
wherein comparing the detected vector and the defined vector comprises comparing the similarity to the tolerance.

6. The method of claim 5 , wherein the similarity is a distance, and further comprising:
mapping the distance to a value that is directly proportional to a degree of similarity between the defined vector and the detected vector; and
wherein comparing the detected vector and the defined vector comprises comparing the value to the tolerance.

7. The method of claim 1 , further comprising:
obtaining multiple defined vectors;
associating each of the multiple defined vectors with a respective tolerance;
interrelating multiple ones of the tolerances; and
wherein comparing the detected vector and the defined vector comprises comparing the detected vector to a region in space described by the interrelated multiple ones of the tolerances.

8. The method of claim 7 , wherein interrelating multiple ones of the tolerances comprises using one or more logical functions.

9. The method of claim 7 , wherein the region in space is defined by a toroid and at least one cone.

10. The method of claim 1 , further comprising:
associating multiple tolerances with the defined vector, each indicating a relationship to the defined vector;
evaluating a condition;
selecting one of the multiple tolerances based on the evaluation; and
wherein comparing the detected vector and the defined vector comprises determining whether the detected vector satisfies a relationship to the defined vector indicated by the selected one of the multiple tolerances.

11. The method of claim 10 , further comprising:
obtaining at least one other defined vector; and
wherein evaluating the condition comprises comparing the detected vector to the at least one other defined vector.

12. The method of claim 11 , further comprising:
determining whether the detected vector is closer to a defined vector for a Face Up posture or a defined vector for a Face Down posture; and
selecting a size of a region in space disposed about a defined vector for a Lying Down posture based on the determination; and
wherein comparing the detected vector and the defined vector comprises comparing the detected vector to the region in space to determine whether the patient is in the Lying Down posture.

13. The method of claim 1 , further comprising:
deriving one or more constants based on at least one of a squared length of the defined vector and a size of a cone surrounding the defined vector;
associating the one or more constants with the defined vector; and
using the one or more constants to compare the detected vector and the defined vector.

14. The method of claim 13 , further comprising normalizing the defined vector to have a selected length.

15. The method of claim 1 , further comprising:
obtaining multiple defined vectors;
comparing the detected vector to ones of the multiple defined vectors; and
if, based on the comparison, the patient is not classified as being in a posture state associated with any of the ones of the multiple defined vectors, generating refinement information to identify to which of the ones of the multiple defined vectors the detected vector is closest.

16. The method of claim 1 , further comprising:
obtaining multiple defined vectors;
processing ones of the multiple defined vectors to obtain a virtual vector; and
comparing the detected vector and the virtual vector to classify the posture state of the patient.

17. The method of claim 16 , wherein comparing the detected vector and the defined vector determines whether the patient is in an Upright posture, and further comprising comparing the detected vector to the virtual vector to determine whether the patient is in a Lying Down posture.

18. The method of claim 17 , further comprising:
obtaining multiple additional defined vectors, each being associated with a posture that may be assumed when the patient is prone; and
if the patient is classified as being in the Lying Down posture, comparing the detected vector to one or more of the additional defined vectors to classify the patient as being in one of the postures that may be assumed when the patient is prone.

19. The method of claim 16 , wherein comparing the detected vector to the virtual vector comprises determining whether the detected vector falls within any one of multiple regions of space disposed about the virtual vector.

20. The method of claim 16 , wherein ones of the multiple defined vectors are each associated with a respective posture the patient may assume when the patient is prone;
obtaining a cross-product between multiple adjacent pairs of the ones of the multiple defined vectors; and
averaging the cross-products to obtain the virtual vector.

21. The method of claim 1 , further comprising:
obtaining multiple defined vectors;
classifying the posture state of the patient based on a comparison between the detected vector and each of the multiple defined vectors; and
if the patient is not classified as being in a posture state associated with any of the multiple defined vectors, generating posture refinement information to identify to which of the multiple defined vectors the detected vector is closest.

22. The method of claim 1 , further comprising:
obtaining multiple defined vectors;
deriving an inner product between the detected vector and multiple ones of the defined vectors; and
based on the derived inner products, classifying the patient as being in a posture state associated with the one

Assignees

• Panken Eric J
• Skelton Dennis M
• Medtronic Inc

Inventors

• Panken Eric J
• Skelton Dennis M

Classifications

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61N1/36542 Primary

  controlled by body motion, e.g. acceleration · CPC title

• A61B5/686

  Permanently implanted devices, e.g. pacemakers, other stimulators, biochips (A61B5/6861 takes precedence) · CPC title

• A61B5/1118

  Determining activity level · CPC title

• A61N1/36535

  controlled by body position or posture · CPC title