Firearm Sensor System for Automatic Event Categorization
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Solution Overview
Problem
Current systems lack the ability to automatically record and analyze firearm-related data, associate it with individual users, and provide feedback to improve shooting form and accuracy.
Innovation Solution
A system comprising sensors attached to a firearm to record movement and force data, which is then analyzed to categorize events such as 'Finger Placed on Trigger' and 'Shot Fired', and generate a user profile for shooting trends and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If body-worn cameras are used to record law enforcement encounters, then the amount of data available for analysis increases, but the cameras may malfunction or become separated from the officer and fail to provide accurate recording
Solution Approach 1:
The system divides the recording function into two separate components: a body-worn camera for video recording and a firearm-mounted sensor system for event detection and data collection. This segmentation ensures that if one component fails, the other can still provide critical information about the encounter.
Solution Approach 2:
The patent introduces an intermediary analysis engine that processes data from multiple sources (camera, firearm sensors, environmental sensors) and correlates them to determine what actually occurred. This intermediary layer reconciles potentially conflicting data from different recording devices.
2Loss of information
If body-worn cameras are used to capture law enforcement encounters, then more data is available, but the camera field of view is not wide enough to capture all officer movements and actions
Solution Approach 1:
The system merges data from multiple sources including firearm-mounted sensors that detect manipulations, environmental sensors that detect sounds and movements, and body-worn camera footage. This combination provides a comprehensive view of the encounter that overcomes the limited field of view of any single device.
Solution Approach 2:
The patent adds temporal and contextual dimensions to the recording system by using sensors that detect the sequence and nature of firearm manipulations, complementing the visual spatial information from the camera. This multi-dimensional approach captures events that may be outside the camera's field of view.
3Measurement precision
If traditional methods like gunshot residue analysis and fingerprint extraction are used, then forensic evidence is gathered, but it is unclear which weapon was used and when the shot was fired
Solution Approach 1:
The patent replaces traditional mechanical forensic methods (gunshot residue analysis, fingerprint extraction) with electronic sensing systems that directly detect and record firearm events. Sensors mounted on the firearm detect manipulations such as magazine changes, safety engagements, and trigger pulls, providing precise timing and identification of which weapon was used.
Solution Approach 2:
The system provides immediate feedback through electronic sensors that detect and record each manipulation of the firearm in real-time, creating a chronological sequence of events. This feedback loop allows for precise determination of which weapon was used and when shots were fired, eliminating the ambiguity of traditional forensic methods.
4Measurement precision
If civilian users fire at targets and evaluate bullet patterns, then shooting accuracy can be assessed, but it is difficult to determine why the pattern is scattered or precise without accurate data
Solution Approach 1:
The system enables civilian users to automatically analyze their own shooting form through sensors that detect manipulations of the firearm. The analysis engine processes the sensor data to identify patterns in grip, stance, and trigger control, providing self-service feedback that helps users improve their shooting accuracy without requiring external coaches or complex manual analysis.
Data Source
AI summary
Systems and methods for analyzing and categorizing firearm-related events are provided herein. A data collection device may be attached to a firearm. The data collection device may be outfitted with sensors to track the movements and forces of the firearm. The movement and force data may be analyzed to categorize the event. Machine learning techniques may be used to stores relationships between the data and the events. A profile for a user may be created that learns the firearm handling techniques of the user. The data collection device may interface with body cameras and other external equipment and may be used in law enforcement scenarios. The data collection device may also be used with civilians for shot analysis.