Computer Systems and Computer-Implemented Methods of Use Thereof Configured to Recognize User Activity During User Interaction with Electronic Computing Devices

1 . A computer-implemented method, comprising:

continuously obtaining, by at least one processor, a visual input comprising a plurality of representations of at least one eye of at least one user to continuously track the plurality of representations over a predetermined time duration;

wherein the visual input comprises a series of video frames, a series of images, or both;

continuously applying, by the at least one processor, at least one eye-gaze movement tracking (EGMT) algorithm to the visual input to form a time series of eye-gaze vectors;

continuously inputting, by the at least one processor, the time series of eye-gaze vectors into an Activity Tracking Neural Network (ATNN) to:

classify at least one activity of the at least one user over the predetermined time duration; and

output a measure of the at least one user's engagement with the classified activity.

2 . The method of claim 1 , wherein the visual input further comprises a plurality of representations of at least one additional facial feature of the at least one user, wherein the at least one additional facial feature is chosen from at least one of: eye gaze, head pose, a distance between a user's face and at least one screen, head posture, at least one detected emotion, or combinations thereof.

3 . The method of claim 2 , further comprising, by the at least one processor, continuously applying to the visual input, at least one facial feature algorithm, wherein the at least one facial feature algorithm is chosen from at least one of: at least one face detection algorithm, at least one face tracking algorithm, at least one head pose estimation algorithm, at least one emotion recognition algorithm, or combinations thereof.

4 . The method of claim 3 , wherein application of the at least one facial feature algorithm transforms the representation of the at least one additional facial feature of the at least one user into at least one additional facial feature vector associated with the at least one additional facial feature, wherein the at least one facial feature vector is chosen from: at least one face angle vector, at least one facial coordinate vector, or a combination thereof.

5 . The method of claim 4 , further comprising, with the at least one processor, continuously obtaining a time series of additional facial feature vectors.

6 . The method of claim 1 , wherein the plurality of representations comprises at least one eye movement of at least one user.

7 . The method of claim 1 , wherein the ATNN is trained using a plurality of representations of a plurality of users, wherein each representation depicts each user engaged in at least one activity.

8 . The method of claim 1 , wherein the predetermined time duration ranges from 1 to 300 minutes.

9 . The method of claim 1 , wherein the at least one eye gaze vector comprises at least two reference points, the at least two reference points comprising:

a first reference point corresponding to an eye pupil; and

a second reference point corresponding to an eye center.

10 . The method of claim 1 , wherein the at least one eye gaze vector is a plurality of eye gaze vectors, wherein the plurality of eye gaze vectors comprises at least one first eye gaze vector corresponding to a first eye and at least one second eye gaze vector corresponding to a second eye.

11 . The method of claim 10 , further comprising a step of, by the at least one processor, averaging the at least one first eye gaze vector and the at least one second eye gaze vector.

12 . The method of claim 1 , wherein the at least one activity is chosen from: reading, watching video, surfing the internet, writing text, programming, or combinations thereof.

13 . A system comprising:

a camera component, wherein the camera component is configured to acquire a visual input, wherein the visual input comprises a real-time representation of at least one eye of at least one user and wherein the visual input comprises at least one video frame, at least one image, or both;

at least one processor;

a non-transitory computer memory, storing a computer program that, when executed by the at least one processor, causes the at least one processor to:

continuously apply at least one eye-gaze movement tracking (EGMT) algorithm to the visual input to form a time series of eye-gaze vectors;

continuously input the time series of eye-gaze vectors into an Activity Tracking Neural Network (ATNN) to determine an attentiveness level of the at least one user over the predetermined time duration to:

classify at least one activity of the at least one user over the predetermined time duration; and

output a measure of the at least one user's engagement with the at least one classified activity.

14 . The system of claim 13 , wherein the visual input further comprises a plurality of representations of at least one additional facial feature of the at least one user, wherein the at least one additional facial feature is chosen from at least one of: eye gaze, head pose, a distance between a user's face and at least one screen, head posture, at least one detected emotion, or combinations thereof.

15 . The system of claim 13 , wherein the plurality of representations comprises at least one eye movement of at least one user.

16 . The system of claim 13 , wherein the ATNN is trained using a plurality of representations of a plurality of users, wherein each representation depicts each user engaged in at least one activity.

17 . The system of claim 13 , wherein the at least one eye gaze vector comprises at least two reference points, the at least two reference points comprising:

a first reference point corresponding to an eye pupil; and

a second reference point corresponding to an eye center.

18 . The system of claim 13 , wherein the at least one eye gaze vector is a plurality of eye gaze vectors, wherein the plurality of eye gaze vectors comprises at least one first eye gaze vector corresponding to a first eye and at least one second eye gaze vector corresponding to a second eye.

19 . The system of claim 13 , wherein the at least one processor averages the at least one first eye gaze vector and the at least one second eye gaze vector.

20 . The system of claim 13 , wherein the at least one activity is chosen from: reading, watching video, surfing the internet, writing text, programming, or combinations thereof.