Mobile Medical Lane Training (MMLT) After Action Review System (AAR)
In advancing medical education during lane training, IVIR was contracted by the U.S. Army Research Laboratory’s Simulation and Training Technology Center (ARL STTC) to research, develop and demonstrate the ability to integrate imaging capturing technology with Light Detection and Range (LiDAR) for an After Action Review (AAR) system to provide real-time feedback for medical lane training activity.
Today many training exercises are conducted without sufficient records of the events that occurred during training (student and/or team successes and failures). This limits the ability to provide useful feedback to the trainees in an AAR circumstance. When the events of the training exercise are recorded, then an objective AAR can be conducted making the training event a more effective learning experience.
Sponsor:
Army Research Laboratory, Simulation and Training Technology Center (ARL-STTC)Award Date:
June, 2013Contract #:
W911QX-13-C-0084Status:
DeliveredInitial Research Using LiDAR In Medical Lane Exercise
Expanded Research For Objective AAR Capability
A research and development program investigated the current position tracking and digital image capturing technologies, their potential uses, and their integration with a LiDAR AAR system. The research was conducted on these technologies for fusion within an AAR system, to provide real-time feedback of a 3-D lane training activity. The initial approach is to inventory existing technology and firmly establish the educational requirements based on desired training outcomes for a mobile medical lane training AAR capability.
The LiDAR 3D AAR capability exists as a proof of concept for individual position location under a previous research effort. A variety of audio/visual systems currently exist that record medical task performance.
The selected technologies will be integrated and demonstrated along with the U.S. Army’s Medical Training Evaluation and Review System (MeTER) tactical and clinical skills checklist as a performance assessment. For this project, IVIR partnered with Bolt, Beranek, and Newman (BBN).
