10.1184/R1/6555659.v1
Samarjit Das
Samarjit
Das
Jestin N. Carlson
Jestin N.
Carlson
Fernando de la Torre
Fernando
de la Torre
Jessica K Hodgins
Jessica K
Hodgins
Multimodal feature analysis for quantitative performance evaluation of endotracheal intubation (ETI)
Carnegie Mellon University
2012
Multimodal feature analysis
3D landmark shape
EMG
endotracheal intubation
emergency medicine
2012-03-01 00:00:00
Journal contribution
https://kilthub.cmu.edu/articles/journal_contribution/Multimodal_feature_analysis_for_quantitative_performance_evaluation_of_endotracheal_intubation_ETI_/6555659
<p>Endotracheal intubation (ETI) is a very crucial medical procedure performed on critically ill patients. It involves insertion of a breathing tube into the trachea i.e. the windpipe connecting the larynx and the lungs. Often, this procedure is performed by the paramedics (aka providers) under challenging prehospital settings e.g. roadside, ambulances or helicopters. Successful intubations could be lifesaving, whereas, failed intubation could potentially be fatal. Under prehospital environments, ETI success rates among the paramedics are surprisingly low and this necessitates better training and performance evaluation of ETI skills. Currently, few objective metrics exist to quantify the differences in ETI techniques between providers of various skill levels. In this pilot study, we develop a quantitative framework for discriminating the kinematic characteristics of providers with different experience levels. The system utilizes statistical analysis on spatio-temporal multimodal features extracted from optical motion capture, accelerometers and electromyography (EMG) sensors. Our experiments involved three individuals performing intubations on a dummy, each with different levels of expertise. Quantitative performance analysis on multimodal features revealed distinctive differences among different skill levels. In future work, the feedback from these analysis could potentially be harnessed towards enhancing ETI training.</p>