PROJECT SUMMARY
HAZWOPER emergency response work represents one of the most dangerous jobs in the United States (U.S.)
where, in many cases, emergency medical first responders are expected to deliver immediate care to persons
suffering from acute traumatic injuries and exposure to hazardous substances (e.g., chemical spills). Therefore,
the HAZWOPER standard devotes very specific and detailed attention to training that represents a major
departure from classical emergency medical first responder action.
Although advanced training technologies (ATT) have emerged over the past decade, ranging from mobile to
virtual reality technologies, current HAZWOPER ATT are insufficient at tutoring, debriefing, and quantifiably
evaluating hands-on skill proficiency while, simultaneously, enabling both hands free to practice emergency
medical skills. NIEHS and OSHA require realistic HAZWOPER training that measurably develops hands-on
skill proficiency. Additionally, students who continually practice hands-on clinical skills in simulated
environments and with patient simulators significantly improve their hands-on skill proficiency.
For the NIH SBIR Phase I effort, Juxtopia proposes to build upon preliminary research results to develop an
artificial intelligent (AI) Juxtopia® Intelligent HAZWOPER Instructor (JiHi) that e-evaluates Fire-Fighter
EMTs and Paramedics’ clinical skill proficiency by using deep learning algorithms to auto-interpret granular
data generated from Juxtopia® Imhotep Band (JiBand) armlets and e-instructing first responders by displaying
multimodal andragogical data on Juxtopia® Augmented Reality (AR) Goggles.
Juxtopia hypothesizes that JiHi, that e-trains through AR Goggles and e-evaluates through JiBands, will
measurably augment instructor training and improve emergency medical personnel (e.g., Fire-Fighter EMTs’)
psychomotor skill proficiency while learners practice emergency medical skills in outdoor simulated HAZMAT
environments. To test the hypothesis during the NIH SBIR Phase I effort, Juxtopia and the Maryland Fire
Rescue Institute (MFRI) will answer the following questions: How can a JiHi deliver multi-modal tutoring of
hands-on clinical skills?; How can a JiHi evaluate hands-on clinical skills?; How can a JiHi continually learn
from students?; How can a JiHi e-evaluate correct or incorrect clinical steps from JiBand collected data?; How
can the JiHi JiBand product be sold at an affordable price? To accomplish the proposed NIH SBIR Phase I effort
and answer the aforementioned question, Juxtopia will test the technical and commercial feasibility of JiHi-
JiBand at MFRI facilities.
