The Augmented Anesthesia Machine Visualization and Interactive Debriefing System (AAMVID)

Video Overview : AAMVID

 Through Mixed Reality (MR), collocated  After Action Review (AAR) allows users to review past training experiences in situ with the user’s current, real-world experience. MR enables a user-controlled egocentric viewpoint, a visual overlay of virtual information, and playback of recorded training experiences collocated with the user’s current experience. Collocated AAR presents novel challenges for MR, such as collocating time, interactions, and visualizations of previous and current experiences.

We created a collocated AAR system for anesthesia education, the Augmented Anesthesia Machine Visualization and Interactive Debriefing system (AAMVID). The system was evaluated in two studies by students (n=19) and educators (n=3). The results demonstrate how collocated AAR systems such as AAMVID can: (1) effectively direct student attention and interaction during AAR and (2) provide novel visualizations of aggregate student performance and insight into student understanding for educators

Publications

Quarles, J., S. Lampotang, I. Fischler, P. Fishwick, B.Lok (2008) "Collocated AAR: Augmenting After Action Review with Mixed Reality" IEEE International Symposium on Mixed and Augmented Reality 2008, September 15-19, Cambridge, U.K., 107-116. 

 

Team Members        

The AAM project is multidisciplinary project including researchers from the following fields:

HCI / Mixed Reality

            John Quarles, Ph.D. Student, UTSA CS

Benjamin Lok, Assistant Professor,UF CISE

Modeling and Simulation

Paul Fishwick, Professor, UF CISE

Anesthesiology

Samsun Lampotang, Professor, UF Dept of Anesthesiology

Psychology

Ira Fischler, Professor, UF Dept of Psychology

 

For more information, contact: John Quarles - jpq@cs.utsa.edu

Figures: Left: visualization of aggregate student gaze data. Right: a student performs a collocated after action review of an expert’s past experience.

If people could rewind life and review our mistakes, they might be able to make better decisions in the future. To enable this, we created an immersive visualization approach. We engineered a Mixed Reality-based system that enables users to review past experiences. Users can review a single past experience or many experiences aggregated together.