# US 12,555,225 B2 · Method and apparatus for monitoring of a human or animal subject

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- Number: US 12,555,225 B2
- Document ID: US12555225B2
- Status: Granted
- Kind: B2
- Application number: 17/878,590
- Filed: 2022-08-01
- Published: 2023-01-26
- Granted: 2026-02-17
- Earliest claimed: 2019-01-02
- Assignees: Oxehealth Limited
- Applicants: Oxehealth Limited
- Inventors: Nicholas Dunkley Hutchinson; Simon Mark Chave Jones
- Web page: https://patentgrep.com/patent/US12555225B2

## CPC

- A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)}
- G06T 7/0012: {Biomedical image inspection}
- A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens}
- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)}
- A61B 5/1128: {using image analysis (A61B5/1127 takes precedence)}
- A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms}
- G06T 7/0016: {involving temporal comparison}
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- A61B 5/113: occurring during breathing
- A61B 2503/40: Animals
- G06T 2207/10016: Video; Image sequence
- G06T 2207/20081: Training; Learning
- G06T 2207/30048: Heart; Cardiac
- G06T 2207/30076: Plethysmography
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

## Claims

### Claim 1

A method of monitoring a human or animal subject comprising the steps of:
capturing a video image of the subject consisting of a time series of image frames each image frame comprising a pixel array of image intensity values; and
analysing the video image to determine automatically a vital sign estimate of one or more vital signs of the subject,
wherein the step of determining automatically a vital sign estimate of one or more vital signs of the subject comprises:
analysing the video image to detect a plurality of signals each comprising the variation in image intensity values with time at each of a respective plurality of positions in the video image;
determining a first plurality of signal quality indices of the signals and retaining only those signals whose signal quality indices are above a predetermined threshold;
analysing the retained signals in a multi-dimensional component analysis to obtain components thereof and retaining a predetermined number of the strongest components;
determining a second plurality of signal quality indices of the retained components;
determining from said second plurality of signal quality indices a confidence value for each of said retained components;
selecting amongst the retained components using the confidence values, wherein the selecting comprises weighting said retained components by the respective confidence values, and updating a prior estimate of said one or more vital signs by said weighted components;
determining the frequency of each of the selected components; and
outputting the vital sign estimate based on said determined frequencies.

### Claim 2 (depends on 1)

A method according to claim 1 wherein each of said plurality of signals comprises the variation in intensity at a plurality of pixels in a local neighbourhood in each of said image frames whose image intensity values are combined together to form one of said plurality of signals.

### Claim 3 (depends on 1)

A method according to claim 1 wherein said positions are positions in the video image at which subject movement has been detected.

### Claim 4 (depends on 1)

A method according to claim 1 further comprising the step of frequency filtering each of said plurality of signals to exclude those outside predetermined expected physiological range for said one or more vital signs.

### Claim 5 (depends on 1)

A method according to claim 1 further comprising the step of calculating the logarithm of said image intensity values to form a logarithm image and said step of analysing the video image is performed on said logarithm image.

### Claim 6 (depends on 1)

A method according to claim 1 wherein said first plurality of signal quality indices comprise measures of periodicity, for example one or more of peak consistency and amplitude consistency.

### Claim 7 (depends on 1)

A method according to claim 1 wherein said step of multi-dimensional component analysis comprises decomposing the retained signals into their components, for example by one of principal component analysis or independent component analysis.

### Claim 8 (depends on 1)

A method according to claim 1 wherein said second plurality of signal quality indices comprise measures of one or more of: periodicity, spatial distribution within the video image, uniformity and variability of the component, amount of movement associated with the component.

### Claim 9 (depends on 1)

A method according to claim 1 further comprising the step of down-weighting said prior estimate of said one or more vital signs by a predetermined amount before updating said prior estimate with said weighted components.

### Claim 10 (depends on 9)

A method according to claim 9 further comprising the step of detecting the amount of subject movement in the image and varying said predetermined amount in dependence upon the detected amount of subject movement.

### Claim 11 (depends on 9)

A method according to claim 9 wherein the amount of subject movement is detected by determining the amount of variation in image intensity with time at each of said respective plurality of positions in the video image.

### Claim 12 (depends on 1)

A method according to claim 1 wherein said step of determining from said second plurality of signal quality indices a confidence value for each of said retained components is performed using a machine learning technique.

### Claim 13 (depends on 1)

A system for monitoring a human or animal subject in accordance with the method of claim 1, comprising:
a video camera adapted to capture a video image of the subject;
a display;
a video image processing unit adapted to process the video image in accordance with the method of claim 1.