| NSF Org: |
TI Translational Impacts |
| Recipient: |
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| Initial Amendment Date: | July 29, 2024 |
| Latest Amendment Date: | July 29, 2024 |
| Award Number: | 2409291 |
| Award Instrument: | Cooperative Agreement |
| Program Manager: |
Alastair Monk
amonk@nsf.gov (703)292-4392 TI Translational Impacts TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | August 1, 2024 |
| End Date: | July 31, 2026 (Estimated) |
| Total Intended Award Amount: | $999,647.00 |
| Total Awarded Amount to Date: | $999,647.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
221 EASY ST APT 10 MOUNTAIN VIEW CA US 94043-3772 (224)622-7263 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
221 EASY ST MOUNTAIN VIEW CA US 94043-3770 |
| 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: |
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| 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 Project (SBIR) Phase II project is to develop a technology that trains law enforcement officers for better stress regulation and decision-making in stressful situations. Police officers are tasked to confront highly stressful, potentially lethal encounters regularly, however, they may not receive sufficient stress management training leading to poor tactical decision-making, unnecessary use of force, and tension between themselves and the communities they serve. Officers also accumulate stress over time leading to a high prevalence of mental and physical health issues including PTSD, substance abuse, and cardiovascular problems. Agencies across the country are under immense pressure to revamp their training to meet the public demand for better behavior by officers. The technology being developed under this proposal monitors officer stress via a wearable sensor. Using state-of-the-art neuroscience and machine learning algorithms calculates if the officer?s stress level is sub-optimal for performance and offers insights and interventions for improvement. This project will enhance our understanding of how physiology impacts police officer performance and the best methods to improve performance under stress. It is serving a rapidly growing U.S. law enforcement training market
of $540,000M.
The proposed project will complete the development and testing of a novel biofeedback-based law enforcement training program. The project goals are (1) Developing and implementing brief and effective stress management interventions such as guided audio scripts on the mobile application. These interventions will be coupled to algorithms that measure stress regulation. Their usability, feasibility, and effectiveness in reducing immediate stress during officer training will be measured. (2) Improving existing
machine learning algorithms that predict poor performance from physiological and other training data to offer more actionable data to trainers. With better predictions, trainers can identify officers needing more training and deliver more targeted training with objective data. These algorithms can also be used in the academy setting for selecting recruits. (3) Testing the effectiveness of using biofeedback in combination with stress management techniques on police key officer performance and wellness metrics. The effectiveness of this novel training on tactical decision making, appropriate use of force, defensive tactics, perceived stress and anxiety will be evaluated. Upon completion of the mentioned tasks, a stress management training program specifically designed for law enforcement training will be developed and scientifically validated.
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.
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