# US 12,007,185 B1 · Weapon usage monitoring system having discharge event monitoring

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- Number: US 12,007,185 B1
- Document ID: US12007185B1
- Status: Granted
- Kind: B1
- Application number: 18/144,917
- Filed: 2023-05-09
- Granted: 2024-06-11
- Earliest claimed: 2017-01-27
- Assignees: Armaments Research Company, Inc.
- Applicants: Armaments Research Company, Inc.
- Inventors: John Asbach; Michael Canty
- Web page: https://patentgrep.com/patent/US12007185B1

## CPC

- F41A 17/063: {comprising a transponder}
- F41A 17/06: Electric or electromechanical safeties (F41A17/04, F41A17/08 take precedence)
- F41A 19/01: Counting means indicating the number of shots fired
- F41A 19/10: Triggers; Trigger mountings
- F41A 21/48: Barrel mounting means, e.g. releasable mountings for replaceable barrels
- F41A 31/00: Testing arrangements (testing mounts F41A23/16)
- F41A 33/00: Adaptations for training (adaptations of barrels for recoil reinforcement F41A21/26); Gun simulators (teaching or practice apparatus for gun-aiming or gun-laying F41G3/26 {; shooting games A63F9/02; military simulation G09B9/003})
- F41C 23/16: Forestocks; Handgrips; Hand guards (pump-action guns F41C7/02)
- F41C 27/04: Arrangements for mounting spades or shields (spades per se A01B1/02; shields per se F41H5/06)
- F41G 3/04: for dispersing fire from a battery {; for controlling spread of shots; for coordinating fire from spaced weapons}
- F41G 3/142: {based on observation of a first shoot; using a simulated shoot (training F41G3/26)}
- F41G 3/165: {using a TV-monitor}
- G01S 19/54: using carrier phase measurements; using long or short baseline interferometry
- H04N 5/28: Mobile studios
- H04N 23/66: Remote control of cameras or camera parts, e.g. by remote control devices
- G01S 19/13: Receivers
- F41A: FUNCTIONAL FEATURES OR DETAILS COMMON TO BOTH SMALLARMS AND ORDNANCE, e.g. CANNONS; MOUNTINGS FOR SMALLARMS OR ORDNANCE
- F41C: SMALLARMS, e.g. PISTOLS, RIFLES (functional features or details common to both smallarms and ordnance, mountings therefor F41A; projecting missiles without use of explosive or combustible propellant charge F41B); ACCESSORIES THEREFOR
- F41G: WEAPON SIGHTS; AIMING (optical aspects thereof G02B)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

A system and method for determining a discharge event of a firearm includes receiving, by an event detection module, acceleration input signals and rotation input signals. An acceleration vector magnitude is calculated from the acceleration signals. The acceleration vector magnitude is compared to a threshold acceleration. A sample event candidate is assigned to the acceleration vector magnitude based on the comparing. The sample event candidate comprises the acceleration input signals and the rotation input signals for a predetermined duration of time. The sample event candidate is received at a machine learning module. Characteristics of the discharge event detected are determined including ammunition type based on the acceleration and rotation input signals in the sample event candidate.

## Claims

### Claim 1

A method for determining a discharge event of a firearm, the method comprising:
receiving, by an event detection module, (i) acceleration input signals including a first acceleration input signal along a first axis, a second acceleration input signal along a second axis and a third acceleration input signal along a third axis; and (ii) rotation input signals including a first rotation input signal around the first axis, a second rotation input signal around the second axis and a third rotation input signal around the third axis;
calculating an acceleration vector magnitude from the acceleration signals;
comparing the acceleration vector magnitude to a threshold acceleration;
assigning a sample event candidate to the acceleration vector magnitude based on the comparing, the sample event candidate comprising the acceleration input signals and the rotation input signals for a predetermined duration of time;
receiving, at a machine learning module, the sample event candidate; and
determining characteristics of the discharge event detected including ammunition type based on the acceleration and rotation input signals in the sample event candidate.

### Claim 2 (depends on 1)

The method of claim 1 wherein the predetermined duration of time is based on a cycle time of the firearm.

### Claim 3 (depends on 1)

The method of claim 1 further comprising, receiving a supplemental input signal including at least one of a pressure input signal, a high-G input signal and an audio signal.

### Claim 4 (depends on 3)

The method of claim 3, further comprising:
determining an occurrence of a shot based on the acceleration and rotation input signals in the sample event candidate and wherein determining the occurrence of a shot is further based on the supplemental input signal.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
determining additional characteristics wherein the ammunition type includes one of a live ammunition and a blank ammunition.

### Claim 6 (depends on 5)

The method of claim 5, further comprising:
determining an occurrence of a reloading event based on the acceleration and rotation input signals in the sample event candidate.

### Claim 7 (depends on 5)

The method of claim 5, further comprising:
determining an occurrence of a weapon malfunction based on the acceleration and rotation input signals in the sample event candidate.

### Claim 8 (depends on 1)

The method of claim 1 wherein the machine learning module includes a multiclass model that is initially trained with a generic model specific to the firearm, the method further comprising:
retraining the model based on user-specific firearm use data.

### Claim 9

A system for providing discharge monitoring of a firearm, the system comprising:
a sensor disposed on the firearm that senses (i) first, second and third accelerations and (ii) first second and third rotations, wherein the first, second and third accelerations are sensed along first, second and third axes, respectively and the first, second and third rotations are sensed around the first, second and third axes, respectively; and
an event detection module that receives signals from the sensor indicative of the first, second and third accelerations, wherein the event detection module is configured to:
calculate an acceleration vector magnitude from the acceleration signals;
compare the acceleration vector magnitude to a threshold acceleration;
assign a candidate event to the acceleration vector magnitude based on the comparing, the candidate event comprising the acceleration input signals and the rotation input signals for a predetermined duration of time;
receive, at a machine learning module, the sample event candidate;
determine ammunition type based on the acceleration and rotation input signals in the sample event candidate.

### Claim 10 (depends on 9)

The system of claim 9 wherein the predetermined duration of time is based on a cycle time of the firearm.

### Claim 11 (depends on 9)

The system of claim 9 wherein the event detection module is further configured to receive a supplemental input signal including at least one of a pressure input signal, a high-G input signal and an audio signal.

### Claim 12 (depends on 11)

The system of claim 11 wherein the occurrence of a shot is further based on the supplemental input signal.

### Claim 13 (depends on 9)

The system of claim 9 wherein the event detection module is further configured to:
determine an occurrence of a shot based on the acceleration and rotation input signals in the sample event candidate.

### Claim 14 (depends on 9)

The system of claim 9 wherein the event detection module is further configured to determine an occurrence of a reloading event based on the acceleration and rotation input signals in the sample event candidate.

### Claim 15 (depends on 9)

The system of claim 9 wherein the event detection module is further configured to determine an occurrence of weapon malfunction based on the acceleration and rotation input signals in the sample event candidate.

### Claim 16 (depends on 9)

The system of claim 9 wherein the machine learning module includes a multiclass model that is initially trained with a generic model specific to the firearm, wherein the event detection module is further configured to retrain the model based on user-specific firearm use data.