Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

cannulation log dimensionless jerk medical training simulator motion smoothness skill metrics spectral arc length

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2021
Historique:
received: 02 11 2020
accepted: 09 02 2021
entrez: 3 5 2021
pubmed: 4 5 2021
medline: 4 5 2021
Statut: epublish

Résumé

Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop "smart" training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (

Identifiants

pubmed: 33937348
doi: 10.3389/frobt.2021.625003
pii: 625003
pmc: PMC8085519
doi:

Types de publication

Journal Article

Langues

eng

Pagination

625003

Informations de copyright

Copyright © 2021 Singh, Bible, Liu, Zhang and Singapogu.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Simar Singh (S)

Department of Bioengineering, Clemson University, Clemson, SC, United States.

Joe Bible (J)

Department of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, United States.

Zhanhe Liu (Z)

Department of Bioengineering, Clemson University, Clemson, SC, United States.

Ziyang Zhang (Z)

Department of Bioengineering, Clemson University, Clemson, SC, United States.

Ravikiran Singapogu (R)

Department of Bioengineering, Clemson University, Clemson, SC, United States.

Classifications MeSH