| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | August 28, 2019 |
| Latest Amendment Date: | October 20, 2022 |
| Award Number: | 1926683 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Benaiah Schrag
bschrag@nsf.gov (703)292-8323 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | September 1, 2019 |
| End Date: | December 31, 2022 (Estimated) |
| Total Intended Award Amount: | $750,000.00 |
| Total Awarded Amount to Date: | $1,449,994.00 |
| Funds Obligated to Date: |
FY 2021 = $699,994.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
6422 BROAD ST BETHESDA MD US 20816-2608 (206)395-4868 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6422 Broad Street Bethesda MD US 20816-2608 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | SBIR Phase II |
| Primary Program Source: |
01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project lies ultimately in saving lives. This project proposes a Machine Learning-enabled Internet of Things (IoT) firearms detection system for law enforcement. Implementation of this technology would significantly reduce the cognitive burden of police officers in dangerous situations, allowing for more informed decisions. Additionally, there are benefits to the warfighter and security professionals to improve their capabilities in keeping the nation and public safe. Successful implementation and commercialization of a firearms detection system will grant capabilities to objectively monitor and leverage firearm usage data to find insights previously unknown, and to provide a data-driven approach towards real-time reactions around firearms and firearm usage.
The proposed project aims to research and develop a Machine Learning-enabled, integrated IoT system dedicated to detecting and processing small arms firearm activity, such as discharges and unholsters. The challenges are two-fold: 1) to develop and iterate based on pilot user feedback regarding the hardware and software portions of the system; and 2) to employ machine learning on a unique dataset for insights on firearms knowledge and handling. The proposed research and development plan calls for pilots with several law enforcement agencies, hardware and software iterations based on user feedback, and research into a real-life dataset collected by police officers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Armaments Research Company, Inc. (ARC) completed its research on ?IoT System for Small Arms Detection and Response? supported by the NSF SBIR Phases I and II commercialization grants. During this most recent funding period, ARC worked with law enforcement agencies from Maryland, Utah, California, and Arizona as well as technology and weapons manufacturing partners to develop a commercialization path for a capability that collects firearms usage data for both law enforcement and military applications. Specifically, ARC sought to validate the need for its product feature set, meet existing and inspire new stakeholder requirements, and unlock various funding sources to enable agencies access to this technology for testing pilots.
ARC achieved its goal of developing and validating a system comprised of a low-cost, reliable sensor capable of highly accurate discharge detection combined with bespoke software reporting. The sensor successfully embedded into the firearm transparent to the end user enabling unencumbered application. Additionally, ARC developed firearm shot detection, draw, and holstering algorithms that reached 97%+ accuracy during stakeholder prioritized scenarios. Lastly, ARC developed a cloud-based data store supporting administrative and customer deployment backend services and APIs to support customer deployment.
As the product evolved to meet stakeholder needs, new opportunities to streamline firearm reporting emerged. ARC created the Firearm Activity Incident Report (FAIR) which maps an entirely new class of firearms usage data to assist in operational analysis, training, and investigations. FAIR data will empower law enforcement leaders to create targeted de-escalation training, discover trends, and identify accountability opportunities. Finally, this data will complement existing technology such as Body Worn Cameras using event-based tagging to streamline analysis and clarify ambiguous accounts.
To enable long-term, programmed access to this technology, ARC developed a grant strategy yielding promising results. ARC?s joint proposals, including National Police Foundation and law enforcement agency sponsorship resulted in two grant awards: the DOJ ?Community Trust and Legitimacy? micro grant and DOJ ?Smart Policing? grant. Additionally, ARC exclusively partnered with Sig Sauer who was subsequently awarded the U.S. Army?s Next Generation Squad Weapon contract to outfit 250,000 soldier rifles with ARC?s sensor technology.
ARC?s research charts the course for the first scaled tactical deployment of embedded firearm usage sensors in the U.S military and establishes an unmet need within the law enforcement industry. This research has demonstrated the viability of and demand for such technology thus creating momentum toward broader use and meaningful societal impact.
Last Modified: 06/11/2023
Modified by: Michael Canty
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