SYSTEM AND METHOD FOR DETERMINING NAVICULAR DROP MEASUREMENTS

1 . A method comprising:

capturing, via an image device associated with a user device, first image data of a portion of a human body in a first state and second image data of the portion of the human body in a second state, the first state different than the second state;

determining, based at least in part on the first image data, a first metric associated with the portion of the human body while the portion is in the first state;

determining, based at least in part on the second image data, a second metric associated with the portion of the human body while the portion is in the second state;

determining, based at least in part on the first metric and the second metric, a third metric associated with the change in state of the portion of the human body; and

outputting the third metric.

2 . The method of claim 1 , wherein:

the portion of the human body is at least one foot;

the first state is in a non-load bearing state; and

the second state is in a load bearing state.

3 . The method of claim 1 , further comprising:

generating, based at least in part on the first image data, a first three-dimensional model of the portion of the human body; and

generating, based at least in part on the second image data, a second three-dimensional model of the portion of the human body.

4 . The method of claim 1 , wherein:

the portion of the human body is at least one foot;

the first metric is height of an arch of the foot in the non-load bearing state;

the second metric is a height of the arch of the foot in the load bearing state; and

the third metric is value representative of a navicular drop of an arch of the foot.

5 . The method of claim 4 , further comprising:

detecting a first ground plane associated with the first three-dimensional model;

filtering mesh points associated with the first three-dimensional model within a threshold distance from the first ground plane to generate a first set of remaining mesh points;

determining, based at least in part on the first set of remaining mesh points and the first ground plane, a first footprint associated with the foot in the non-load bearing state;

determining, based at least in part on the first three-dimensional model, a first convex hull associated with the foot in the non-load bearing state; and

determining, based at least in part on the first footprint and the first convex hull, a medial border line of the foot in the non-load bearing state.

6 . The method of claim 5 , further comprising:

detecting a second ground plane associated with the second three-dimensional model;

filtering mesh points associated with the second three-dimensional model within the threshold distance from the second ground plane to generate a second set of remaining mesh points;

determining, based at least in part on the second set of remaining mesh points and the second ground plane, a second footprint associated with the foot in the load bearing state;

determining, based at least in part on the second three-dimensional model, a second convex hull associated with the foot in the load bearing state; and

determining, based at least in part on the second footprint and the second convex hull, a medial border line of the foot in the load bearing state.

7 . The method of claim 6 , wherein detecting the first ground plane is associated with a first technique and detecting the second ground plane is associated with a second technique different than the first technique.

8 . The method of claim 7 , wherein:

determining the first footprint associated with the foot in the non-load bearing state further comprises projecting the first set of mesh points onto the first ground plane.

9 . The method of claim 8 , wherein determining the second footprint associated with the foot in the load bearing state further comprises projecting the second set of mesh points onto the second ground plane.

10 . The method of claim 7 , further comprising:

determining a first intersection point by projecting the medial border line of the foot in the non-load bearing state onto the first three-dimensional model;

determining a mid-point of the medial border line of the foot in the non-load bearing state;

determining a first distance between the first intersection point and the mid-point of the medial border line of the foot in the non-load bearing state, the first distance being the first metric;

determining a second intersection point by projecting the medial border line of the foot in the load bearing state onto the second three-dimensional model;

determining a mid-point of the medial border line of the foot in the load bearing state;

determining a second distance between the second intersection point and the mid-point of the medial border line of the foot in the load bearing state, the second distance being the second metric; and

determining a difference between the first distance and the second distance.

11 . The method of claim 5 , wherein the threshold distance is at least one of the following:

approximately 1.5 centimeters; or

approximately 1.0 centimeters.

12 . The method of claim 1 , wherein:

the portion of the human body is at least one foot;

determining the first metric associated with the foot while the foot is in the non-load bearing state is based at least in part on the first three-dimensional model; and

determining the second metric associated with the foot while the foot is in the load bearing state is based at least in part on the second three-dimensional model.

13 . The method of claim 1 , wherein the portion of the human body is at least one foot and generating the first three-dimensional model and the second three-dimensional model further comprises:

inputting the first image data into one or more machine learned models trained on image data of feet at various load bearing stages and receiving from the one or more machine learned models the first three-dimensional model; and

inputting the second image data into the one or more machine learned models and receiving from the one or more machine learned models the second three-dimensional model.

14 . The method of claim 1 , wherein the portion of the human body is at least one foot and determining the first metric, the second metric, or the third metric is further comprises utilizing one or more machine learning models trained on image data of feet in load bearing and non load bearing states of various different individuals having various lifestyles, health status, and demographics.

15 . One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving first image data of a foot in a non-load bearing state and second image data of the foot in a load bearing state;

determining, based at least in part on the first image data, a first height of an arch of the foot while the foot is in the non-load bearing state;

determining, based at least in part on the second image data, a second height of an arch of the foot while the foot is in the load bearing state;

determining, based at least in part on the first metric and the second metric, a third metric associated with the arch of the foot; and

outputting the third metric.

16 . The one or more non-transitory computer-readable media of claim 15 , wherein the third metric is a navicular drop of the arch of the foot.

17 . A system comprising:

one or more processors; and

one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

receiving first sensor data of a foot in a non-load bearing state and second sensor data of the foot in a load bearing state;

determining, based at least in part on the first sensor data, a first height of an arch of the foot while the foot is in the non-load bearing state;

determining, based at least in part on the second sensor data, a second height of an arch of the foot while the foot is in the load bearing state;

determining, based at least in part on the first metric and the second metric, a third metric associated with the arch of the foot; and

outputting the third metric.

18 . The system of claim 17 , wherein the operations further comprise:

determining the first metric associated with the arch of the foot is based at least in part on first-three dimensional model generated from the first image data; and

determining the second metric associated with the arch of the foot is based at least in part on second-three dimensional model generated from the second image data.

19 . The system of claim 17 , wherein:

the system is a cloud-based system that is remote from a user equipment; and

first sensor data and the second sensor data is received from the user equipment.

20 . The system of claim 17 , wherein the first sensor data and the second sensor data is one or more one of the following:

image data;

thermal data;

depth data;

infrared data;

magnetic resonance imaging data, or

LIDAR data.