# US 2019/0318601 A1 · Surveillance system and method for predicting patient falls using motion feature patterns

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- Number: US 2019/0318601 A1
- Document ID: US20190318601A1
- Status: Published application
- Kind: A1
- Application number: 16/353,485
- Filed: 2019-03-14
- Published: 2019-10-17
- Earliest claimed: 2017-07-10
- Assignees: CareView Communications, Inc.
- Applicants: CareView Communications, Inc.
- Inventors: Steven Gail Johnson; Derek del Carpio
- Web page: https://patentgrep.com/patent/US20190318601A1

## CPC

- G08B 21/043: {detecting an emergency event, e.g. a fall}
- G06T 7/248: {involving reference images or patches}
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- G06V 30/194: References adjustable by an adaptive method, e.g. learning
- G08B 21/0476: {Cameras to detect unsafe condition, e.g. video cameras}
- G08B 29/186: {Fuzzy logic; neural networks}
- G06T 2207/10016: Video; Image sequence
- G06T 2207/30196: Human being; Person
- G06T 2207/30232: Surveillance
- G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## 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

### Claim at position 1

1-20. (canceled)

### Claim 21

A surveillance system for detecting a fall risk condition, the system comprising:
a computer system comprising memory and logic circuitry configured to:
retrieve, from a storage device, video data that is associated with triggering false alarm cases of patient falls;
determine motion feature patterns of the video data associated with the false alarm cases;
store the motion feature patterns of the video data;
analyze, using a classifier that is trained with a set of features associated with triggering alarms for patient falls, a plurality of frames generated from a surveillance camera;
classify one or more of the plurality of frames as causing an alarm; and
verify the alarm by extracting motion feature patterns of the one or more of the plurality of frames and comparing the extracted motion feature patterns with the stored motion feature patterns.

### Claim 22 (depends on 21)

The system of claim 21 wherein the computer system is further configured to receive the plurality of frames from the surveillance camera, the plurality of frames including an area in which a patient at risk of falling is being monitored.

### Claim 23 (depends on 21)

The system of claim 21 wherein the false alarm cases include motion of individuals other than a patient in bed.

### Claim 24 (depends on 21)

The system of claim 21 wherein the false alarm cases include a healthcare provider walking into a room and past a bed, or out a door.

### Claim 25 (depends on 21)

The system of claim 21 wherein the false alarm cases include visitors getting up off a chair that is adjacent to a bed.

### Claim 26 (depends on 21)

The system of claim 21 wherein the false alarm cases include an individual sitting on a chair that appears on top or across a rail of a bed.

### Claim 27 (depends on 21)

The system of claim 21 wherein the false alarm cases include a patient sitting on a bed with the patient's legs across an edge of the bed.

### Claim 28 (depends on 21)

The system of claim 21 wherein the false alarm cases include a tray table moving back and forth on a rail of a bed.

### Claim 29 (depends on 21)

The system of claim 21 wherein the computer system is further configured to:
retrieve, from the storage device, video data that is associated with triggering real alarm cases of patient falls;
determine motion feature patterns of the video data associated with the real alarm cases; and
store the motion feature patterns of the video data associated with the real alarm cases.

### Claim 30 (depends on 29)

The system of claim 29 wherein the real alarm cases include patients rolling over near an edge of a bed or moving off the bed.

### Claim 31 (depends on 21)

The system of claim 21 wherein the computer system is further configured to update the classifier based on the verification of the alarm.

### Claim 32 (depends on 21)

The system of claim 21 wherein the computer system is further configured to retrieve the video data based on historical data associated with the video data.

### Claim 33 (depends on 21)

The system of claim 21 wherein the computer system is further configured to label frames in the video data to indicate the false alarm cases.

### Claim 34 (depends on 33)

The system of claim 33 wherein the computer system executes unsupervised clustering techniques to label the frames in the video data.

### Claim 35 (depends on 21)

The system of claim 21 wherein the computer system is further configured to verify the alarm based on accelerometer information worn by a patient corresponding to the plurality of frames.

### Claim 36 (depends on 21)

The system of claim 21 wherein the computer system is further configured to classify consecutive frames from the plurality of frames as causing the alarm.

### Claim 37

A method for detecting a fall risk condition, the method comprising:
retrieving, from a storage device, video data that is associated with triggering false alarm cases of patient falls;
determining motion feature patterns of the video data associated with the false alarm cases;
storing the motion feature patterns of the video data;
analyzing, using a classifier that is trained with a set of features associated with triggering alarms for patient falls, a plurality of frames generated from a surveillance camera;
classifying one or more of the plurality of frames as causing an alarm; and
verifying the alarm by extracting motion feature patterns of the one or more of the plurality of frames and comparing the extracted motion feature patterns with the stored motion feature patterns.

### Claim 38

A system for detecting a fall risk condition, the system comprising:
a computer system comprising memory and logic circuitry configured to:
retrieve, from a storage device, video data that is associated with triggering false alarm cases of patient falls;
determine motion feature patterns of the video data associated with the false alarm cases; and
store the motion feature patterns of the video data in a database stored on a memory unit accessible to one or more classifier servers over a network, the one or more classifier servers verify one or more of a plurality of frames generated from a surveillance camera as causing an alarm based on the stored motion feature patterns

### Claim 39 (depends on 38)

The system of claim 38 wherein the computer system is further configured to label frames in the video data to indicate the false alarm cases.

### Claim 40 (depends on 38)

The system of claim 38 wherein the computer system is further configured to:
retrieve, from the storage device, video data that is associated with triggering real alarm cases of patient falls;
determine motion feature patterns of the video data associated with the real alarm cases; and
store the motion feature patterns of the video data associated with the real alarm cases in the database.