Granted Patent B2

US 12,555,225 · App. 17/878,590 · Granted Feb 17, 2026

Inventors: Nicholas Dunkley Hutchinson (Oxford, GB); Simon Mark Chave Jones (Oxford, GB)

Assignee: OXEHEALTH LIMITED

G06T7/0012A61B5/0077A61B5/02416A61B5/0816A61B5/1128A61B5/746G06T7/20G06T7/70A61B2503/40G06T2207/10016G06T2207/20081G06T2207/30048

Abstract

Claims (27)

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.

2 . 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.

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

4 . 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.

5 . 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.

6 . 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.

7 . 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.

8 . 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.

9 . 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.

10 . 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.

11 . 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.

12 . 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.

13 . 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 .

Priority Claims (1)

Continuity (2)

References Cited (162)