Automated Shooting Coach Using Machine Learning for Error Detection
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Solution Overview
Problem
Current shooting training methods rely heavily on human instructors to identify and correct shooting errors, which can be time-consuming and inefficient, especially when training multiple shooters simultaneously, particularly on live ranges where diagnostic sensors are limited.
Innovation Solution
The development of automated coaching systems that utilize machine learning algorithms, such as Artificial Neural Networks, to analyze shot dispersion data and sensor data from cameras and other sensors, predicting and displaying potential shooting mistakes to both instructors and shooters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human instructors manually analyze each trainee's shooting technique, then shooting errors can be identified and corrected, but the training process becomes time-consuming and inefficient when multiple shooters are trained simultaneously
Solution Approach 1:
The system enables automated self-assessment of shooting techniques by capturing sensor data, analyzing it through machine learning algorithms, and providing feedback without requiring continuous human instructor intervention. Each shooter's performance is automatically evaluated against ideal techniques, allowing the system to serve itself in the coaching function.
Solution Approach 2:
The patent replaces the mechanical system of manual visual inspection and physical correction by human instructors with an automated electronic system that uses sensors, computer vision, and machine learning algorithms to detect, analyze, and provide feedback on shooting errors.
2Reliability
If human instructors closely monitor multiple trainees simultaneously, then shooting errors can be detected, but the quality of error detection deteriorates due to the high number of trainees
Solution Approach 1:
The system performs automated self-monitoring of shooting techniques by continuously capturing sensor data from weapons, shooters, and environments, analyzing it through machine learning models, and generating feedback without requiring human instructor attention for each individual shooter.
Solution Approach 2:
The patent substitutes the human instructor's visual and cognitive monitoring system with an automated electronic monitoring system that uses multiple sensors, computer vision cameras, and machine learning algorithms to reliably detect and analyze shooting errors across multiple trainees simultaneously.
3Measurement precision
If diagnostic sensors are added to weapons for detailed analysis, then shooting error identification improves, but the system complexity and cost increase
Solution Approach 1:
The system employs multi-functional sensors that serve multiple purposes: weapon sensors capture trigger pull force, rate of fire, and weapon orientation; shooter sensors monitor body position, breathing patterns, and head orientation; environmental sensors track lighting and wind conditions. Each sensor type serves multiple diagnostic functions, reducing the need for specialized single-purpose instruments.
Solution Approach 2:
The patent replaces complex mechanical diagnostic instrumentation with electronic sensors and computational analysis. Instead of using multiple specialized mechanical devices to measure different shooting parameters, the system uses electronic sensors coupled with machine learning algorithms to extract multiple diagnostic indicators from the sensor data.
4Productivity
If immediate feedback on shooting errors is provided to shooters, then training efficiency improves, but the system requires automated real-time analysis capabilities
Solution Approach 1:
The system implements continuous feedback loops where sensor data from weapons, shooters, and environments is captured in real-time, analyzed by machine learning algorithms to identify deviations from ideal shooting techniques, and immediately communicated back to shooters through visual or audible feedback, enabling rapid correction of errors.
Solution Approach 2:
The patent replaces the mechanical system of delayed manual feedback (where instructors observe and then provide correction after training sessions) with an automated real-time electronic feedback system that processes sensor data through machine learning models and delivers immediate corrective information to shooters during training.
Data Source
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AI summary
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.