# US 11,998,364 B2 · Image-based system and method for predicting physiological parameters

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- Number: US 11,998,364 B2
- Document ID: US11998364B2
- Status: Granted
- Kind: B2
- Application number: 17/339,887
- Filed: 2021-06-04
- Published: 2021-09-23
- Granted: 2024-06-04
- Earliest claimed: 2017-04-05
- Assignees: Sharecare AI, Inc.
- Applicants: Sharecare AI, Inc.
- Inventors: Walter De Brouwer; Apurv Mishra; Samia De Brouwer
- Web page: https://patentgrep.com/patent/US11998364B2

## CPC

- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens}
- A61B 5/1032: {Determining colour of tissue for diagnostic purposes}
- A61B 5/1072: {measuring distances on the body, e.g. measuring length, height or thickness (A61B5/1076 takes precedence)}
- A61B 5/441: {Skin evaluation, e.g. for skin disorder diagnosis}
- A61B 5/4872: {Body fat}
- A61B 5/7267: {involving training the classification device}
- G06F 18/24133: {Distances to prototypes}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N 3/09: Supervised learning
- G06N 3/096: Transfer learning
- G06N 5/046: Forward inferencing; Production systems
- G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]}
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 40/169: {Holistic features and representations, i.e. based on the facial image taken as a whole}
- G06V 40/172: {Classification, e.g. identification}
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/30: for calculating health indices; for individual health risk assessment
- A61B 5/024: Measuring pulse rate or heart rate
- A61B 5/02405: {Determining heart rate variability}
- A61B 5/163: {by tracking eye movement, gaze, or pupil change}
- A61B 5/442: {Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment}
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06V 40/178: {estimating age from face image; using age information for improving recognition}
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

System and method for determining physiological parameters of a person are disclosed. A physiological parameter may be obtained by analyzing a facial image of a person, and determining, from the facial image, a physiological parameter of the person by processing the facial image with a data processor. A neural network model such as regression deep learning convolutional neural network is used to predict the physiological parameter. An image processor screens out images which can't be recognized as facial images and adjust facial images to frontal facial images for predicting of physiological parameters.

## Claims

### Claim 1

A system for predicting physiological parameter of a person based on a facial image thereof, comprising:
an image processor, electrically coupled with a network, configured to:
receive from a digital device an image including facial and upper body features of a person;
process said image to generate a frontal facial image;
provide said frontal facial image, comprising facial and upper body features, to a trained neural network model configured to predict a gender classification of said person based on said facial and upper body features;
receive said gender classification of said person from said neural network model; and wherein
said trained neural network model is a regression deep learning convolutional neural network model.

### Claim 2 (depends on 1)

The system of claim 1, wherein said neural network model has a plurality of input parameters, said input parameters including three color channels corresponding to one or more images.

### Claim 3 (depends on 1)

The system of claim 1 wherein said gender classification is presented to an end-user via a digital device.

### Claim 4 (depends on 1)

The system of claim 1, wherein said regression deep learning convolutional neural network model is a Network-in-Network neural network model.

### Claim 5 (depends on 1)

The system of claim 1, wherein said frontal facial image is provided via three color channels.

### Claim 6 (depends on 1)

The system of claim 1, further comprising a server, electrically coupled with said network, and wherein said trained neural network model is stored on the server.

### Claim 7 (depends on 1)

The system of claim 1, further comprising a digital device configured to capture an image including facial and upper body features of said person, wherein said digital device is electrically coupled with said network.

### Claim 8 (depends on 1)

The system of claim 1, wherein said image processor is further configured to evaluate said image to determine if said image is a qualified image of said person.

### Claim 9 (depends on 8)

The system of claim 8, wherein said neural network model predicts said gender classification of said person based upon said qualified image of said person.

### Claim 10

A system for predicting physiological parameter of a person based on a facial image thereof, comprising:
an image processor, electrically coupled with a network, configured to:
receive from a digital device an image including facial and upper body features of a person;
process the image to generate a frontal facial image;
provide the frontal facial image, comprising the facial and upper body features, to a trained neural network model configured to predict an age classification of the person based on the facial and upper body features;
receive said age of the person from said neural network model;
and wherein
said trained neural network model is a regression deep learning convolutional neural network model.

### Claim 11 (depends on 10)

The system of claim 10, wherein said neural network model has a plurality of input parameters including three color channels corresponding to one or more images.

### Claim 12 (depends on 10)

The system of claim 10, wherein said age classification is represented by an age group.

### Claim 13 (depends on 10)

The system of claim 10 wherein said age classification is presented to an end-user via a digital device.

### Claim 14 (depends on 10)

The system of claim 10, wherein the regression deep learning convolutional neural network model is a Network-in-Network neural network model.

### Claim 15 (depends on 10)

The system of claim 10, wherein said frontal facial image is provided via three color channels.

### Claim 16 (depends on 10)

The system of claim 10, further comprising a server, electrically coupled with said network, wherein the trained neural network model is stored on the server.

### Claim 17 (depends on 10)

The system of claim 10, further comprising a digital device configured to capture an image including a facial and upper body features of the person, wherein the digital device is electrically coupled with said network.

### Claim 18 (depends on 10)

The system of claim 10, wherein said image processor is further configured to evaluate said image to determine if said image is a qualified image of the person.

### Claim 19 (depends on 18)

The system of claim 18 wherein said age classification represents a ten year range of age.

### Claim 20

A method for predicting physiological parameter of a person based on a facial image thereof, comprising:
receiving a request for a gender classification or an age classification;
acquiring an image with at least facial and upper body features of a person; processing said image to a frontal facial image;
applying the frontal facial image, comprising said facial and upper body features, to a trained neural network model to predict said gender classification and said age classification of the person based on the facial and upper body features;
providing, in response to said request, said gender classification or said age classification; and wherein
said trained neural network model is a regression deep learning convolutional neural network model.