Surveillance system and method for predicting patient falls using motion feature patterns

• US 10,276,019 B2
• Filed: 07/24/2018
• Issued: 04/30/2019
• Est. Priority Date: 07/10/2017
• Status: Active Grant

• Current Assignee: Careview Communications Incorporated
• Original Assignee: Careview Communications Incorporated
• Inventors: Johnson, Steven Gail, del Carpio, Derek
• Primary Examiner(s): Vu, Kim Y
• Assistant Examiner(s): Delaney, Molly
• Application Number:
  US16/043,965
• Publication Number:
  US 20190012893A1
• Time in Patent Office: 280 Days
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 7/248 involving reference images or patches
• 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 30/194 References adjustable by an adaptive method, e.g. learning
• CPC Class Codes: G08B 21/043 detecting an emergency event, e.g. a fall
• CPC Class Codes: G08B 21/0476 Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks

Abstract

A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.

Claims

1. A surveillance system for detecting a fall risk condition, the system comprising:

a storage device that stores motion feature patterns that are extracted from video recordings of patient activities, the motion feature patterns each being representative of motion associated with a risk event or a non-risk event based on a centroid of motion corresponding to movement proximate to a fall risk area; and

at least one server comprising;

a classifier component that generates a fall alert by analyzing a plurality of frames generated by a surveillance camera, and

an alarm verifier that

determines a centroid of motion of one or more frames from the plurality of frames that correspond to the fall alert,

compares the centroid of motion of the one or more frames with the motion feature patterns, and

determines whether to confirm the fall alert based on the comparison.

2. The system of claim 1 wherein the alarm verifier is configured to identify real alarm cases or false-alarm cases of fall events based on accelerometer information worn by a patient corresponding to the video recordings.

3. The system of claim 1 wherein the alarm verifier is configured to determine statistically significant similarities in centroid features between the centroid of motion of the one or more frames and the motion feature patterns.

4. The system of claim 1 wherein the classifier component is configured to:

detect motion of pixels by comparing pixels of a current frame with at least one previous frame; and

mark pixels that have changed as a motion pixel in a given motion image.

5. The system of claim 1 wherein the at least one server is configured to extract the motion feature patterns by calculating at least one of a centroid location, centroid area, connected components ratio, bed motion percentage, and unconnected motion.

6. The system of claim 5 wherein the at least one server is further configured to calculate the centroid location by computing weighted average x and y coordinates of motion pixels in a given motion image.

7. The system of claim 5 wherein the at least one server is further configured to calculate the bed motion percentage by calculating a ratio of motion pixels from a given motion image within a virtual bed zone to a total pixel count in the virtual bed zone.

8. The system of claim 5 wherein the at least one server is further configured to extract the motion feature patterns includes the computer system configured to:

group motion pixels that are connected in a given motion image into clusters; and

prune motion pixels from the given motion image that do not have at least one pixel within a threshold distance of a virtual bed zone.

9. The system of claim 5 wherein the at least one server is further configured to determine the connected components ratio by calculating a ratio of motion pixels outside the virtual bed zone to motion pixels inside the virtual bed zone.

10. The system of claim 5 wherein the at least one server is further configured to determine the unconnected motion by calculating an amount of motion pixels in the area of the centroid that is unrelated to connected motion pixels within and near the virtual bed zone.

11. A method for predicting a condition of elevated risk of a fall with a computer system comprising:

storing motion feature patterns that are extracted from video recordings of patient activities, the motion feature patterns each being representative of motion associated with a risk event or a non-risk event based on a centroid of motion corresponding to movement proximate to a fall risk area;

generating a fall alert by analyzing a plurality of frames generated by surveillance camera;

determining centroid of motion of one or more frames from the plurality of frames that correspond to the fall alert;

comparing the centroid of motion of the one or more frames with the motion feature patterns; and

determining whether to confirm the fall alert based on the comparison.

12. The method of claim 11 further comprising identifying real alarm cases or false-alarm cases of fall events based on accelerometer information worn by a patient corresponding to the video recordings.

13. The method of claim 11 wherein comparing the centroid of motion of the one or more frames with the motion feature patterns further comprises determining statistically significant similarities in centroid features.

14. The method of claim 11 wherein determining the centroid of motion of the one or more frames further comprises:

detecting motion of pixels by comparing pixels of a current frame with at least one previous frame; and

marking pixels that have changed as a motion pixel in a given motion image.

15. The method of claim 11 further comprising extracting the motion feature patterns by calculating at least one of a centroid location, centroid area, connected components ratio, bed motion percentage, and unconnected motion.

16. The method of claim 15 further comprising calculating the centroid location by computing weighted average x and y coordinates of motion pixels in a given motion image.

17. The method of claim 15 further comprising calculating the bed motion percentage by calculating a ratio of motion pixels from a given motion image within a virtual bed zone to a total pixel count in the virtual bed zone.

18. The method of claim 15 wherein extracting the motion feature patterns further comprises:

grouping motion pixels that are connected in a given motion image into clusters; and

pruning motion pixels from the given motion image that don'"'"'t have at least one pixel within a threshold distance of the virtual bed zone.

19. The method of claim 15 further comprising determining the connected components ratio by calculating a ratio of motion pixels outside the virtual bed zone to motion pixels inside the virtual bed zone.

20. The method of claim 15 further comprising determining the unconnected motion by calculating an amount of motion pixels in the area of the centroid that is unrelated to connected motion pixels within and near the virtual bed zone.