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User Experience (UX)

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The User Experience (UX) portfolio strives for first responders to have tools designed around their specific context, tasks, and requirements, based on user-centered design and feedback. From augmented reality (AR) and virtual reality (VR) to heads up displays (HUDs), haptics, and audio cues, this portfolio focuses on training and testing of smart systems to further develop first responder-machine interactions.

Portfolio Problem Statement: Public safety communications technologies are often misaligned with first responders’ real-world needs due to limited user-centered design, resulting in tools that are difficult to use in mission-critical situations.

Mission:

Work with public safety community, stakeholders, and vendors to create an environment that encourages industry to develop reliable, intuitive, and mission-focused technology for the public safety community.

Vision:

Enhance the ability of the public safety user to effectively interact with and obtain information from the system. Ensure public safety devices are designed around operational needs through innovation and collaboration with industry and the public safety community.

Featured Project

Evaluating Enhanced Public Safety User Interfaces using a Digital Twin

Project Goal: Develop concrete design guidelines for user interfaces (UIs) in Self-Contained Breathing Apparatus (SCBA) masks to enhance situational awareness and response effectiveness, focusing initially on navigation UIs.

Why it’s hard

Since 1990, 390 line-of-duty deaths have been attributed to “Caught or Trapped” (363), “Out of Air” (8), and “Lost” (19). This project addresses the need for more effective situational awareness, response coordination, and navigational information during emergencies, reducing first responder and victim casualties. These enhanced UIs do not exist today and are a challenge to measure in a real-world environment. A digital twin allows for robust study in a controlled lab.

Technical capabilities/approach

Co-design navigation-focused SCBA user interfaces with first responders to ensure displays directly support situational awareness, decision-making, and response effectiveness in high-risk
Develop and test navigational heads-up display (HUD) prototypes within the Public Safety Immersive Test Center (PSITC), leveraging a digital twin
Evaluate UI effectiveness using ISO 9241-11 usability metrics
Establish a rigorous, repeatable evaluation framework, grounded in NIST-designed studies and test methods, that can be applied consistently to assess industry SCBA UI solutions

Outcomes

Produce concrete, data-driven design guidance for SCBA navigation UIs
Deliver a trusted, lab-based evaluation process for industry
Inform the development and refinement of public safety standards, including:
- NFPA: 1986 (SCBA), 1930 (Two-Way Communications), and 950 (Data Exchange)
- IEEE Public Safety Technology Initiative: Standards and Testbed Committees
- AREA: Human Factors and Research Working Groups

Public Safety Motion Dataset for Enabling Trustworthy Artificial Intelligence Applications

Project Goal: Develop an open, multi-camera 3D skeletal motion dataset and evaluation suite that powers robust, low‑latency AI for first responders. The results will improve bandwidth utilization, stream selection, and computer vision algorithms for action detection. Future uses could also include serving as research and training datasets for the next generation of robots.

Why it’s hard

Public‑safety related datasets are missing from existing AI benchmarks. This gap prevents the development of reliable next-generation analytics capable of identifying anomalies in public safety response given expected public safety actions. High‑quality, field‑relevant data is essential for NIST’s mission to measure and standardize trustworthy AI.

Technical capabilities/approach

Collaborate with with Fire & Rescue and Police Departments to design and execute 20+ realistic incident-response scenarios
Capture high-fidelity motion data in the PSITC using synchronized multi-view 4K video and OptiTrack systems
Perform synchronization audits, data cleaning, 3D skeleton extraction, and coordinate registration to produce a high-quality, field-relevant dataset suitable for benchmarking trustworthy AI
Apply multi-level action labels using an LLM-assisted pipeline
Define performance metrics and develop baseline models for 2D-to-3D pose lifting and action detection
Train and evaluate a reinforcement-learning (RL) agent for dynamic camera view-selection to optimize reconstruction quality vs. bandwidth
Conduct usability and realism validation session with first responders

Outcomes

Release a comprehensive, open dataset to support reproducible and trustworthy AI research for public safety
Enable standardized evaluation of computer vision and action-detection algorithms under public-safety–relevant constraints
Disseminate findings through peer-reviewed publications and active engagement with IEEE standards working groups
Provide industry, academia, and government stakeholders with validated data and tools to improve AI-enabled situational awareness and decision support for first responders

Publications

Quick Resources

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Advanced communications, Information technology, Usability and human factors, Virtual / augmented reality and Public safety

Created September 18, 2017, Updated June 8, 2026

Public Safety Communications Research Division