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An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education

The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting...

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Autores principales: Lemos, Juan Diego, Hernandez, Alher Mauricio, Soto-Romero, Georges
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469341/
https://www.ncbi.nlm.nih.gov/pubmed/28468268
http://dx.doi.org/10.3390/s17050988
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author Lemos, Juan Diego
Hernandez, Alher Mauricio
Soto-Romero, Georges
author_facet Lemos, Juan Diego
Hernandez, Alher Mauricio
Soto-Romero, Georges
author_sort Lemos, Juan Diego
collection PubMed
description The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee’s performance. For this reason, it is necessary to develop automatic mechanisms to estimate metrics for assessing manual dexterity in a quantitative way. Authors have proposed some hardware-software approaches to evaluate manual dexterity on surgical simulators. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. It has been designed to be used with a neurosurgical simulator called Daubara NS Trainer, but can be easily adapted to another benchtop- and manikin-based medical simulators. The system was tested with a sample of 14 volunteers who performed a test that was designed to simultaneously evaluate their fine motor skills and the IGlove’s functionalities. Metrics obtained by each of the participants are presented as results in this work; it is also shown how these metrics are used to automatically evaluate the level of manual dexterity of each volunteer.
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spelling pubmed-54693412017-06-16 An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education Lemos, Juan Diego Hernandez, Alher Mauricio Soto-Romero, Georges Sensors (Basel) Article The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee’s performance. For this reason, it is necessary to develop automatic mechanisms to estimate metrics for assessing manual dexterity in a quantitative way. Authors have proposed some hardware-software approaches to evaluate manual dexterity on surgical simulators. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. It has been designed to be used with a neurosurgical simulator called Daubara NS Trainer, but can be easily adapted to another benchtop- and manikin-based medical simulators. The system was tested with a sample of 14 volunteers who performed a test that was designed to simultaneously evaluate their fine motor skills and the IGlove’s functionalities. Metrics obtained by each of the participants are presented as results in this work; it is also shown how these metrics are used to automatically evaluate the level of manual dexterity of each volunteer. MDPI 2017-04-29 /pmc/articles/PMC5469341/ /pubmed/28468268 http://dx.doi.org/10.3390/s17050988 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lemos, Juan Diego
Hernandez, Alher Mauricio
Soto-Romero, Georges
An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title_full An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title_fullStr An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title_full_unstemmed An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title_short An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education
title_sort instrumented glove to assess manual dexterity in simulation-based neurosurgical education
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469341/
https://www.ncbi.nlm.nih.gov/pubmed/28468268
http://dx.doi.org/10.3390/s17050988
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