IP Library › Granted Patent US 10,430,942
Granted Patent B2
US 10,430,942 · App. 15/026,723 · Granted Oct 1, 2019

Image analysis for predicting body weight in humans

Inventors: Jeffrey S. Barrett (North Wales, PA); Chee Ng (Princeton Junction, NJ)
Assignee: University of Kentucky Research Foundation
G06T7/0012A61B5/0062A61B5/0077A61B5/0082A61B5/1072A61B5/7275G06F19/00G06K9/00281G06K9/66G06T11/60G16H50/20A61B5/7267G06K2009/00322G06T2207/30004
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Quick Facts
Patent No.
US 10,430,942
App. No.
15/026,723
Granted
Oct 1, 2019
Kind
B2
Abstract

Systems and methods for determining body weight predictions and human conditions are disclosed. A body weight may be predicted by capturing at least one image of a human, and determining, from the image, a body weight prediction of the human by processing the at least one image with a data processor. The body weight prediction may further be based on an age-based weight factor. A model such as a neural network model may be used to predict body weight.

Claims (35)

1. A method for predicting body weight of a human subject, the method comprising:

capturing an image of a face of the subject;

receiving an age factor of the subject;

determining a weight factor for the received age factor from an age-based weight prediction model; determining at least three facial feature measurement factors for the face from the captured image, the at least three facial feature measurement factors including a first facial feature measurement factor extending in a first direction, a second facial feature measurement factor extending in a second direction different from the first direction, and a third facial feature measurement factor extending substantially in the same direction as the second facial measurement factor; and applying the weight factor and the at least three facial feature measurement factors to a trained neural network model to predict the weight of the subject human, the trained neural network model trained with parameters corresponding to the at least three facial feature measurement factors and the weight factor obtained from each of a plurality of other human subjects; wherein the face includes a right eye, a left eye, a nose, lips, and a chin, and wherein the facial feature measurement factors include:

the nose to the lips;

the lips to the chin;

a right edge of the face in-line with the lips to a left edge of the face in-line with the lips;

a right edge of the face in-line with the nose to a left edge of the face in-line with the nose;

a right edge of the face in-line with a midpoint between the lips and the chin to a left edge of the face in-line with a midpoint between the lips and the chin;

one half the sum of:

the right eye to the nose, and

the left eye to the nose; and

one half the sum of:

the right eye to the lips, and

the left eye to the lips.

2. The method of claim 1 further comprising determining a medication dose, intravenous fluid requirement, or voltage prediction for the subject human based on the determined weight prediction for the human subject.

3. A system for predicting body weight of a human subject, the system comprising:

a camera to capture images,

a data input device,

memory for storing an age-based weight prediction model and a trained neural network model, the trained neural network model trained with parameters corresponding to at least two facial feature measurement factors and a weight factor obtained from each of a plurality of other humans, the at least two facial feature measurement factors including a first facial feature measurement factor extending in a first direction and a second facial feature measurement factor extending in a second direction different from the first direction;

a data processor coupled to the data input device, the camera, and the memory, the data processor configured to capture an image of a face of the human subject with the camera, receive an age factor of the human subject with the data input device; determine the weight factor for the received age factor from the stored age-based weight prediction model; determine the at least two facial feature measurement factors for the face of the human subject from the captured image; and apply the weight factor and the at least two facial feature measurement factors to the trained neural network model to predict the weight of the human subject,

further comprising additional facial feature measurement factors, wherein the face includes a right eye, a left eye, a nose, lips, and a chin, and wherein the facial feature measurement factors include:

the nose to the lips;

the lips to the chin;

a right edge of the face in-line with the lips to a left edge of the face in-line with the lips;

a right edge of the face in-line with the nose to a left edge of the face in-line with the nose;

a right edge of the face in-line with a midpoint between the lips and the chin to a left edge of the face in-line with a midpoint between the lips and the chin;

one half the sum of:

the right eye to the nose, and

the left eye to the nose; and

one half the sum of:

the right eye to the lips, and

the left eye to the lips.

4. The system of claim 3 , further comprising determining a medication dose, intravenous fluid requirement, or voltage prediction for the subject human based on the determined weight prediction for the human subject.

5. The method of claim 1 , further comprising determining a medication dose, intravenous fluid requirement, or voltage prediction for the subject human based on the determined weight prediction for the human subject.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2019
From: NG, CHEE; BARRETT, JEFFREY S
To: UNIVERSITY OF KENTUCKY RESEARCH FOUNDATION
Reel/Frame 048915/0081 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2018
From: THE CHILDREN'S HOSPITAL OF PHILADELPHIA RESEARCH INSTITUTE
To: NG, CHEE; BARRETT, JEFFERY
Reel/Frame 045566/0001 →
Continuity (2)
Provisional Application 61885222 · Oct 1, 2013
Related Publication 20160253798A1 · Sep 1, 2016