Development of a Hand Motion-based Assessment System for Endotracheal Intubation Training

Endotracheal intubation (ETI) is a procedure to manage and secure an unconscious patient's airway. It is one of the most critical skills in emergency or intensive care. Regular training and practice are required for medical providers to maintain proficiency. Currently, ETI training is assessed by human supervisors who may make inconsistent assessments. This study aims at developing an automated assessment system that analyzes ETI skills and classifies a trainee into an experienced or a novice immediately after training. To make the system more available and affordable, we investigate the feasibility of utilizing only hand motion features as determining factors of ETI proficiency. To this end, we extract 18 features from hand motion in time and frequency domains, and also 12 force features for comparison. Subsequently, feature selection algorithms are applied to identify an ideal feature set for developing classification models. Experimental results show that an artificial neural network (ANN) classifier with five hand motion features selected by a correlation-based algorithm achieves the highest accuracy of 91.17% while an ANN with five force features has only 80.06%. This study corroborates that a simple assessment system based on a small number of hand motion features can be effective in assisting ETI training.

Automated summary

Endotracheal intubation is a critical skill in emergency or intensive care, and this study explores the feasibility of assessing ETI proficiency using hand motion features. Experimental results show that an artificial neural network classifier based on a small number of hand motion features achieves a high accuracy rate.