Systems and methods for automated coaching of a shooter
A method for automatically predicting the cause of suboptimal shooting is provided. In some embodiments, the method comprises: providing a plurality of good example reference data to an evaluation function; providing a plurality of bad example reference data to the evaluation function; obtaining training data of a trainee's shot dispersion data; obtaining training data from at least one sensor mounted on the trainee's gun; using the evaluation function to classify the training data as good or bad; and, displaying the classification on a screen as feedback.
1. A method for automatically predicting the cause of suboptimal shooting comprising:
processing a first reference example of a shot dispersion pattern indicating a particular shooting mistake and a second reference example of data from a sensor mounted on a weapon indicating the particular shooting mistake in an evaluation function as bad example reference data;
processing a third reference example of a shot dispersion pattern not indicating the particular shooting mistake and a fourth reference example of data from a sensor mounted on a weapon not indicating the particular shooting mistake in the evaluation function as good example reference data;
calculating a plurality of parameters associated with the first, second, third, and fourth reference examples;
assigning weights to the plurality of parameters;
repeating both processing steps and the calculating step and adjusting the weights until the evaluation function can correctly determine whether a combination of a reference example of a shot dispersion pattern and a reference example of data from a sensor mounted on a weapon indicates the particular shooting mistake or does not indicate the particular shooting mistake;
providing a trainee weapon with at least one sensor mounted on the trainee weapon to a trainee;
obtaining training data that includes shot dispersion data of the trainee and data from the at least one sensor mounted on the trainee weapon;
using the evaluation function to classify the training data as indicating the particular shooting mistake or not indicating the particular shooting mistake; and,
displaying on a screen whether the particular shooting mistake has occurred.
2. The method of claim 1 , wherein the evaluation function is a regression algorithm.
3. The method of claim 1 , wherein the shot dispersion data of the trainee is obtained by a camera and the shot dispersion data of the trainee is further determined using image analysis.
4. The method of claim 1 , wherein the at least one sensor mounted on the trainee weapon measures any one of the group consisting of trigger squeeze pressure or displacement, weapon cant, butt pressure and aim trace.
5. The method of claim 1 , wherein feedback from a plurality of trainees is displayed on a single screen.
6. The method of claim 1 , wherein the evaluation function uses Kernel Discriminant Analysis.
7. The method of claim 1 , wherein the at least one sensor is a trigger pull sensor.
8. The method of claim 1 , wherein the evaluation function uses an Artificial Neural Network.
9. A system for automatically predicting the cause of suboptimal shooting comprising:
a database with a plurality of reference examples for suboptimal shooting mistakes;
a trainee weapon for a trainee;
at least one sensor mounted to the trainee weapon;
a display including a screen;
a computer with software that when executed by a processor performs the following steps:
processing from the database a first reference example of a shot dispersion pattern indicating a particular suboptimal shooting mistake and a second reference example of data from a sensor mounted on a weapon indicating the particular suboptimal shooting mistake in an evaluation function as bad example reference data;
processing from the database a third reference example of a shot dispersion pattern not indicating the particular suboptimal-shooting mistake and a fourth reference example of data from a sensor mounted on a weapon not indicating the particular suboptimal shooting mistake in the evaluation function as good example reference data;
calculating a plurality of parameters associated with the first, second, third, and fourth reference examples;
assigning weights to the plurality of parameters;
repeating both processing steps and the calculating step and adjusting the weights until the evaluation function can correctly determine whether a combination of a reference example of a shot dispersion pattern and a reference example of data from a sensor mounted on a weapon indicates the particular suboptimal shooting mistake or does not indicate the particular suboptimal shooting mistake;
obtaining training data that includes shot dispersion data of the trainee and data from the at least one sensor mounted on the trainee weapon;
classifying the training data using the evaluation function as indicating the particular suboptimal shooting mistake or not indicating the particular suboptimal shooting mistake; and,
displaying on the screen when the particular suboptimal shooting mistake has occurred.
10. The system of claim 9 , wherein the software includes a regression algorithm.
11. The system of claim 9 , wherein the shot dispersion data of the trainee is obtained by a camera.
12. The system of claim 11 , wherein the shot dispersion data of the trainee is further determined using image analysis.
13. The system of claim 9 , wherein the at least one sensor mounted on the trainee weapon includes anyone of the group consisting of trigger squeeze sensor, weapon cant sensor, butt pressure sensor and aim trace sensor.
14. The system of claim 9 , wherein feedback for a plurality of trainees is displayed on a single screen.
15. The system of claim 9 , wherein the evaluation function uses Kernel Discriminant Analysis.
16. The system of claim 9 , wherein the at least one sensor mounted on the trainee weapon is a trigger pull sensor.
17. The system of claim 9 , wherein the evaluation function uses an Artificial Neural Network.
18. The method of claim 1 further comprising wherein the evaluation function calculates an error value based on a difference between a desired response of the evaluation function and an actual response of the evaluation function and the repeating step continues until the error value is below a threshold.
19. The method of claim 1 further comprising confirming or denying a predicted cause of suboptimal shooting by the evaluation function with a shooting instructor.
20. The method of claim 1 , wherein the bad example reference data and the good example reference data further include biometric reference data.