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 hu...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 8; pp. 1 - 11 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151837886&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151837886 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2021 vid: 45 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151837886 151837886 151837886 10.1007/s10916-021-01755-2 151837886 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development of a Hand Motion-based Assessment System for Endotracheal Intubation Training. aug: au: Lim, Chiho Ko, Hoo Sang Cho, Sohyung Ohu, Ikechukwu Wang, Henry E. Griffin, Russell Kerrey, Benjamin Carlson, Jestin N. affil: Department of Industrial Engineering, Southern Illinois University, 62026, Edwardsville, IL, USA sug: subj: Intubation, Intratracheal Education Hand Physiology Motion Analysis Systems Program Development Education, Nursing Descriptive Statistics Intubation, Intratracheal Expert Clinicians Health Services Accessibility Automation Monitoring, Physiologic Time Factors Algorithms Experimental Studies Neural Networks (Computer) Patient Assessment Methods Reliability and Validity Models, Educational Male Female Human Correlational Studies Comparative Studies Correlation Coefficient T-Tests Male Female ab: 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. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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