Application based determination of vital signs and a physiological state with application based action initiation

1 . A method comprising:

capturing, by camera of a smart device, a plurality of images of a body part of a patient;

determining, by the smart device, a plurality of hemoglobin concentration (HC) changes based on the plurality of images;

determining, by the smart device, a set of bitplanes of the plurality of images that represent the plurality of hemoglobin concentration (HC) changes of the patient;

extracting, by the smart device, a value for a vital sign from the plurality of HC changes;

building, by the smart device, a feature set comprising the plurality of HC changes;

performing, by the smart device, a trained machine learning model comprising a computational model on the feature set to obtain an output data set comprising a physiological state for the vital sign;

determining, by the smart device, the physiological state is outside of a threshold range; and

activating, by the smart device, a call module or a video streaming module running on the smart device based on determining the physiological state is outside of the threshold range.

2 . The method of claim 1 , further comprising:

receiving, by the smart device from a machine learning computer, the trained machine learning model.

3 . The method of claim 2 , further comprising:

receiving, by the smart device from the machine learning computer, a second trained machine learning model; and

performing, by the smart device, the second trained machine learning model on additional feature sets to obtain additional output data sets comprising physiological states.

4 . The method of claim 1 , further comprising:

generating, by the smart device, a report containing a value for the vital sign and the physiological state; and

displaying, by the smart device, the report on a display of the smart device.

5 . The method of claim 4 , wherein the report visually characterizes the vital sign relative to the physiological state.

6 . The method of claim 5 , wherein the physiological state is indicated as normal, elevated, or severe in the displayed report.

7 . The method of claim 1 , wherein the machine learning model is trained.

8 . The method of claim 1 , wherein the machine learning model is a K-means clustering model or a neural network model.

9 . The method of claim 1 , further comprising:

receiving, by a machine learning computer from the smart device, the plurality of images;

determining, by a ML training module of the machine learning computer, a second plurality of hemoglobin concentration (HC) changes based on the plurality of images;

determining, by the ML training module of the machine learning computer, a second set of bitplanes of the plurality of images that represent the second plurality of hemoglobin concentration (HC) changes;

extracting, by the ML training module of the machine learning computer, spatial-temporal features from the second set of bitplanes;

creating, by the ML training module of the machine learning computer, a training feature set;

performing, by the ML training module of the machine learning computer, a second machine learning model on the training feature set to generate the computational model.

10 . The method of claim 9 , wherein an output of the second machine learning model is the physiological state.

11 . A computer system comprising a smart device, wherein the smart device is configured to:

capture, by camera of the smart device, a plurality of images of a body part of a patient;

determine, by an application program running on the smart device, a plurality of hemoglobin concentration (HC) changes based on the plurality of images;

determine, by the application program, a set of bitplanes of the plurality of images that represent the plurality of hemoglobin concentration (HC) changes of the patient;

extract, by the application program, a value for a vital sign from the plurality of HC changes;

build, by the application program, a feature set comprising the plurality of HC changes;

perform, by the application program, a trained machine learning model comprising a computational model on the feature set to obtain an output data set comprising a physiological state for the vital sign;

determine, by the application program, the physiological state is outside of a threshold range; and

activate, by the application program, a call module running on the smart device or a video streaming module of the application program based on determining the physiological state is outside of the threshold range.

12 . The computer system of claim 11 , wherein the application program of the smart device is further configured to:

receive, from a machine learning computer, the trained machine learning model.

13 . The computer system of claim 12 , wherein the application program of the smart device is further configured to:

receive, from the machine learning computer, a second trained machine learning model; and

perform the second trained machine learning model on additional feature sets to obtain additional output data sets comprising physiological states.

14 . The computer system of claim 11 , wherein the application program of the smart device is further configured to:

generate a report containing a value for the vital sign and the physiological state; and

display the report on a display of the smart device.

15 . The computer system of claim 14 , wherein the report visually characterizes the vital sign relative to the physiological state.

16 . The computer system of claim 15 , wherein the physiological state is indicated as normal, elevated, or severe in the displayed report.

17 . The computer system of claim 11 , wherein the machine learning model is trained.

18 . The computer system of claim 11 , wherein the machine learning model is a K-means clustering model or a neural network model.

19 . The computer system of claim 11 , further comprising a machine learning computer, wherein the machine learning computer is configured to:

receive, from the smart device, the plurality of images;

determine, by a ML training module of the machine learning computer, a second plurality of hemoglobin concentration (HC) changes based on the plurality of images;

determine, by the ML training module of the machine learning computer, a second set of bitplanes of the plurality of images that represent the second plurality of hemoglobin concentration (HC) changes;

extract, by the ML training module of the machine learning computer, spatial-temporal features from the second set of bitplanes;

create, by the ML training module of the machine learning computer, a training feature set based on the spatial-temporal features;

perform, by the ML training module of the machine learning computer, a second machine learning model on the training feature set to generate the computational model.

20 . The computer system of claim 19 , wherein an output of the second machine learning model is the physiological state.