Systems, methods and apparatus for prevention of injury

1. A computer-based system for determining a prediction of future injury to a knee anterior cruciate ligament of a subject, the computer-based system comprising:

a motion sensing input device generating a plurality of images of the subject, the subject having no markers attached to the subject, the motion sensing input device having photoelectric elements, transfer gates that are responsive to a control pulse for transferring the charges stored on the individual photoelectric elements as an image signal to vertical shift registers and a horizontal shift register for transferring an image signal from the vertical shift registers through a buffer amplifier to an outlet, the plurality of images being captured at least 30 frames per second, each of the plurality of images having a resolution of at least 640×480 pixels and 10 bit depth, having at least 2,048 levels of sensitivity;

a computer having a processor, a memory and input/output capability, the processor being configured to perform:

a pose recognition or a skeletal tracking and calculating of joint angles at different time points;

the processor being further configured to analyze from the plurality of images a motion of the subject, an angle of the knee joint of the subject, jumping and landing mechanics of the subject, and a balance of the subject in reference to either the pose recognition, or the skeletal tracking and calculating of the joint angles at the different time points

the processor being then further configured to determine the prediction of future injury to the knee anterior cruciate ligament of the subject based on the analysis of the plurality of images, wherein the prediction of future injury to the knee anterior cruciate ligament of the subject further comprises an ACL risk score;

the processor being then further configured to display the ACL risk score by a display, or transmit the ACL risk score by a communication subsystem, a transmit the ACL risk score by short-range communications subsystem, enunciate the ACL risk score by a speaker, or store the ACL risk score by a flash memory into the processor;

the processor being then further configured to generate and operate a prediction screen displaying the ACL risk score, wherein the ACL risk score is expressed on the prediction screen as “high risk” or “low risk”;

wherein the processor is configured to determine the prediction of future injury to the knee anterior cruciate ligament by performing machine learning processes utilizing a model trained from a set of data that contains both desired inputs and desired outputs,

in which the set of data for training the model includes images with and without the desired inputs,

and each image includes a label for the desired output designating whether the image contained a desired training object;

wherein the machine learning processes include supervised regression learning which produces continuous outputs, wherein the continuous outputs are a continuous range of values for the ACL risk score.

2. The computer-based system of claim 1 , wherein the computer-based system does not comprise a motion profile, the motion profile further not comprising ranges of node angle value.

3. The computer-based system of claim 1 , wherein the computer-based system does not comprise ranges of node angle value in a motion profile.

4. The computer-based system of claim 1 , wherein the computer-based system does not comprise a motion profile, the motion profile further comprising ranges of node angle value or displacement experienced for one or more exercises.

5. The computer-based system of claim 1 , wherein the computer-based system does not comprise a database of previously recorded motion profiles.

6. The computer-based system of claim 1 , wherein the computer-based system does not determine a rehab/treatment schedule.

7. The computer-based system of claim 1 , wherein the processor being further configured to perform the pose recognition or the skeletal tracking and the calculating joint angles at the different time points and being configured to analyze a gait of the subject, angle of the knee joint of the subject and jumping and the landing mechanics of the subject and the balance of the subject in further reference to other variables including biological gender of the subject, sport played by the subject, and/or previous injury of the subject to determine the prediction of future injury to the knee anterior cruciate ligament of the subject.

8. A computer-based system for determining a prediction of future injury to a knee anterior cruciate ligament of a subject, the computer-based system comprising:

a motion sensing input device generating a plurality of images of the subject, the subject having no markers attached to the subject, the motion sensing input device having photoelectric elements, transfer gates that are responsive to a control pulse for transferring the charges stored on the individual photoelectric elements as an image signal to vertical shift registers and a horizontal shift register for transferring an image signal from the vertical shift registers through a buffer amplifier to an outlet, the plurality of images being captured at least 30 frames per second, each of the plurality of images having a resolution of at least 640×480 pixels and 10 bit depth and having at least 2,048 levels of sensitivity;

a computer having a processor, a memory and input/output capability, the processor being configured to perform:

a pose recognition or a skeletal tracking and calculating of joint angles at different time points;

the processor being further configured to analyze from the plurality of images a motion of the subject, an angle of the knee joint of the subject, jumping and landing mechanics of the subject in reference to either the pose recognition, or the skeletal tracking and calculating of the joint angles at the different time points based on the analysis of the plurality of images;

the processor being then further configured to determine the prediction of future injury to the knee anterior cruciate ligament of the subject based on the analysis of the plurality of images, wherein the prediction of future injury to the knee anterior cruciate ligament of the subject further comprises an ACL risk score;

the processor being then further configured to save the ACL risk score into the memory as “at risk”;

and the processor being then further configured to generate and operate a prediction screen displaying the ACL risk score, wherein the ACL risk score is expressed on the prediction screen as “high risk” or “low risk”;

wherein the processor is configured to determine the prediction of future injury to the knee anterior cruciate ligament by performing machine learning processes utilizing a model trained from a set of data that contains both desired inputs and desired outputs,

in which the set of data for training the model includes images with and without the desired inputs,

and each image includes a label for the desired output designating whether the image contained a desired training object;

wherein the machine learning processes include supervised regression learning which produces continuous outputs, wherein the continuous outputs are a continuous range of values for the ACL risk score.

9. The computer-based system of claim 8 , wherein the computer-based system does not comprise ranges of node angle value in a motion profile.

10. The computer-based system of claim 8 , wherein the computer-based system does not comprise a motion profile, the motion profile further comprising ranges of node angle value or displacement experienced for one or more exercises.

11. The computer-based system of claim 8 , wherein the computer-based system does not comprise a database of previously recorded motion profiles.

12. The computer-based system of claim 8 , wherein the computer-based system does not determine a rehab/treatment schedule.

13. The computer-based system of claim 8 , wherein the processor being further configured to perform the skeletal tracking and calculating of the joint angles at the different time points and the processor then being further configured to analyze a gait of the subject, an angle of the knee joint of the subject, jumping and the landing mechanics of the subject in further reference to other variables including biological gender of the subject, sport played by the subject, and/or previous injury of the subject.

14. The computer-based system of claim 8 , wherein the processor being further configured to determine the prediction of future injury to the knee anterior cruciate ligament of the subject from images captured by a hand-held imaging system.