Multimodal feature analysis for quantitative performance evaluation of endotracheal intubation (ETI)

摘要: Endotracheal intubation (ETI) is a crucial medical procedure performed on critically ill patients. It involves insertion of breathing tube into the trachea i.e. windpipe connecting larynx and lungs. Often, this by paramedics (aka providers) under challenging prehospital settings e.g. roadside, ambulances or helicopters. Successful intubations could be lifesaving, whereas, failed potentially fatal. Under environments, ETI success rates among are surprisingly low necessitates better training performance evaluation skills. Currently, few objective metrics exist to quantify differences in techniques between providers. In pilot study, we develop quantitative framework for discriminating kinematic characteristics providers with different experience levels. The system utilizes statistical analysis spatio-temporal multimodal features extracted from optical motion capture, accelerometers electromyography (EMG) sensors. Our experiments involved three individuals performing dummy, each levels training. Quantitative revealed distinctive skill future work, feedback these harnessed enhanced