PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Human-Machine Interfaces
KEYWORDS
Neuroanalytics; Human Performance; Predictive Analytics; Brain-Computer Interface (BCI); AI-Enabled Coaching; Real-Time Training Adaptation; Cognitive Load Monitoring; Aircrew Readiness; Adaptive Learning; Large Language Model; NAVAIR; NAWCTSD
OBJECTIVE
Develop and demonstrate a neuro-enhanced artificial intelligence (AI) system that captures, analyzes, and operationalizes neurophysiological and behavioral data to provide near real-time, adaptive feedback for improved training efficiency, performance, and operational readiness of U.S. Navy personnel.
DESCRIPTION
The U.S. Navy Force Design 2045 (CNO NavPlan 2024) highlights the importance of the warfighter and human-machine teaming in the future fight, emphasizing the criticality of developing high-performing teams and leaders that are resilient, adaptable, and warrior tough while supporting an increasingly hybrid Fleet of manned assets augmented with thousands of unmanned assets. The future fight will likely require operators to 1) digest and synthesize large amounts of data from an extensive network of humans and machines, 2) make decisions more rapidly due to advances in AI, enhanced connectivity, and autonomous weaponry and 3) oversee a greater number and types of robotics, including swarms (RAND, 2024).
Critical features of this paradigm shift towards manned-unmanned teaming and emphasis on improving warfighter performance are how we train operators. Training is at the forefront of the modernization of Naval operations to enhance readiness and lethality, and this will depend heavily on the cognitive resilience and decision-making capacity of warfighters in these novel, high-stress environments. Traditional training paradigms typically neglect real-time measurement and integration of cognitive and physiological performance states (e.g., mental effort, task engagement, lapses and slips of attention, complacency, mental fatigue, and stress). Emerging technologies for advanced data analytics grounded in neuroscience provide new capability that can enhance warfighter development and mission success by embedding neurofeedback into live and synthetic Naval training environments, providing novel analytical features and data to adapt training in near-real time and accelerate learning at the point of need.
The U.S. Navy seeks to identify a major step forward in neuro-enhanced AI systems to reduce time-to-proficiency and predict Sailor readiness within the unique maritime military environment. This envisioned capability will leverage and further develop Commercial Off-the-Shelf (COTS) neurotechnologies along with complimentary biosensors (e.g., electrocardiography [ECG], electromyography [EMG], eye tracking) and behavioral monitoring tools for Navy-specific use cases to interface with personnel, enabling adaptive and responsive system interaction based on near real-time human state data.
This SBIR topic will prioritize two key demonstrated factors in support of its objective: (1) the ability to collect neural, physiological, and behavioral data in parallel with operators using a desktop or higher fidelity simulator; and (2) the ability to analyze and interact with that data, both in near real-time and post-hoc, using an advanced language-understanding system coupled with an extensive foundational model of the human psychophysiology and/or behavior to provide feedback. This effort will complement existing Navy initiatives, such as those led by NAVAIR, NAWCAD, and NAWCTSD, enhancing existing learning environments through the addition of a brain-based performance layer.
The platform will deliver an autonomous solution for near real-time feedback, improved after-action reporting, and guided adaptation of training scenarios via data standards that can be used to improve understanding of Sailor state (static and dynamic), which will be imperative for improving warfighter performance and training towards an ever-evolving mission in the future fight.
PHASE I
Design and validate a strategy for integrating the neuro-enhanced AI system with existing Navy training architectures (e.g., NAWCTSD training systems or LVC frameworks). Define and characterize mission-relevant cognitive states predictive of optimal warfighter performance. Develop a system architecture that fuses neurophysiological, behavioral, and mission/environmental data for predictive insight.
Deliver system architecture documentation, a feasibility analysis of neuro-enhanced AI system data integration with Naval training systems, a preliminary data model for cognitive and physiological performance state prediction, and a prototype development roadmap for Phase II.
PHASE II
Build and demonstrate a working prototype of the system integrated within a Navy-relevant training environment. Instrument a Naval operational team (e.g., aircrew, ship bridge) for real-time neurophysiological data collection and adaptive training response. Implement a neuro-enhanced advanced language understanding system for AI-driven coaching, guiding warfighter and instructors in near real-time.
Deliver an Institutional Review Board (IRB) application/approval; a cybersecurity and RMF compliance report; data strategy documentation and integration with existing Navy training platforms; a live data collection event demonstrating improvements in performance and mission readiness; updated data exchange framework using API or Navy-compliant standards.
DON will provide Phase II awardees with the appropriate guidance and assistance for human research protocols though the performer will be responsible for obtaining any required Institutional Review Board (IRB) determinations. IRB determination as well as processing, submission, and review of all paperwork required for human subject use can be a lengthy process. As such, no human research will be allowed without IRB review and work will not be authorized until approval has been obtained, typically as an Option to be exercised during Phase II.
PHASE III DUAL USE APPLICATIONS
Validate system effectiveness for improving warfighter performance and readiness; demonstrate adaptive capabilities with AI-based recommendations; achieve authority to operate (ATO) with Navy training platform(s). Support transition of the SBIR-developed neuro-enhanced AI system.
Validated capabilities will be relevant for:
Naval Aviation: Enhancing pilot and flight officer readiness and cognitive workload management.
Surface Fleet Training: Real-time feedback during complex shipboard simulations.
Submarine Operations: Monitoring mental states during long-duration missions.
Medical Teams Afloat: Enhancing high-stakes clinical team performance.
Special Operations Forces: Resilience training and peak performance optimization
Commercial applications include aviation, e-sports, medical simulation, and elite training environments where human performance optimization is critical.
REFERENCES
Chief of Naval Operations Navigation Plan (2024). https://www.navy.mil/leadership/chief-of-naval-operations/cno-navplan-2024/
RAND Corporation (2024).Brain-Computer Interfaces: U.S. Military Applications and Implications. https://www.rand.org/content/dam/rand/pubs/research_reports/RR2900/RR2996/RAND_RR2996.pdf
OUSD R&E(2024). Human Systems Integration Guidebook. https://www.cto.mil/wp-content/uploads/2024/07/HSI-Guidebook-C1-2024.pdf
"DoDI 8510.01 - Risk Management Framework (RMF)." https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001p.pdf
DARPA. "Neurotechnology for Intelligence Analysts (NIA)." https://www.esd.whs.mil/Portals/54/Documents/FOID/Reading%20Room/Science_and_Technology/08-F-0799_Neurotechnology_for_Intelligence_Analysts_NIA_2008.pdf
"Experience API (xAPI) and Learning Record Store (LRS) standards." https://www.google.com/search?q=Experience+API+(xAPI)+and+Learning+Record+Store+(LRS)+standards&rlz=1C1JZAP_enUS1043US1043&oq=Experience+API+(xAPI)+and+Learning+Record+Store+(LRS)+standards&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzk5MGowajSoAgCwAgA&sourceid=chrome&ie=UTF-8
"Ethical AI Principles in DoD Applications." https://www.defense.gov/News/News-Stories/Article/Article/2640609/memo-outlines-dod-plans-for-responsible-artificial-intelligence/
Stephens, C. et al. (2018). Biocybernetic adaptation strategies: Machine awareness of human state for improved operational performance. International Conference on Augmented Cognition. https://doi.org/10.1007/978-3-319-91470-1_9
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