# VR-Based Evaluation and Training System for Emergency Responders and Managers

> **NIH NIH R44** · TIETRONIX SOFTWARE, INC. · 2020 · $199,977

## Abstract

Virtual and Augmented Reality (VR/AR) systems are increasingly being utilized as training
platforms for complex, extremely demanding or rarely executed tasks. Often, VR systems focus
primarily on delivering increasingly realistic scenarios for training purposes without any capability
to assess or refine trainee performance in situ. Our novel VR training platform to deliver HAZMAT
training not only delivers realistic scenarios, but also measures and evaluates performance using
scientifically validated measures of variables associated with both individual and team
performance. The advantage of our approach is to immerse first responders in HAZMAT
emergency scenarios that are realistic and also designed to focus on measurement and refinement
of specific areas of performance. Key contributors to performance among emergency responders
and managers were identified by an extensive review of the literature and subsequent tested for
association by psychometric assessment of over three hundred emergency responders. A subset of
18 highly associated contributors were then identified through statistical analysis of survey results.
These contributors can be measurably represented in VR Training scenario elements. Performance
related to each can then be measured and assessed for individual or team trainees. These refined
key contributors can then be validated on larger, more diverse samples of emergency responders
using the beta version of our proposed VR-based system. Our VR system is also a configurable
platform that enables the evaluation and training of a wide range of skills needed by distinct roles
(police, firefighters, EMTs, etc.) in diverse scenarios such as biosafety spills, HAZMAT disasters
and bioterrorism threats. Also, HAZMAT disasters that are rare or very difficult/costly to create
real world training events can be more easily and cost effectively mastered. Scenarios also can be
dynamically modulated by trainer input in real-time, or by computerized Artificial Intelligence
analysis of performance and trainee real-time physiological measures to rapidly optimize specific
key contributor performance of individuals and teams. Rapid, efficient and effective training of
emergency responders serves the ultimate goal of minimizing potential catastrophic consequences
of these events.

## Key facts

- **NIH application ID:** 9987201
- **Project number:** 2R44ES029348-02
- **Recipient organization:** TIETRONIX SOFTWARE, INC.
- **Principal Investigator:** William Robinson Buras
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $199,977
- **Award type:** 2
- **Project period:** 2018-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987201

## Citation

> US National Institutes of Health, RePORTER application 9987201, VR-Based Evaluation and Training System for Emergency Responders and Managers (2R44ES029348-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9987201. Licensed CC0.

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