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Sensors 2017, 17(5), 988; doi:10.3390/s17050988

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

1
Bioinstrumentation and Clinical Engineering Research Group—GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín 050010, Colombia
2
LAAS-CNRS, Université de Toulouse, CNRS, Toulouse 31400, France
3
ISIFC, Université de Franche-Comté, Besançon 25000, France
*
Author to whom correspondence should be addressed.
Academic Editor: Daniel Teichmann
Received: 31 October 2016 / Revised: 1 April 2017 / Accepted: 7 April 2017 / Published: 29 April 2017
(This article belongs to the Special Issue Wearable Biomedical Sensors)
View Full-Text   |   Download PDF [4626 KB, uploaded 17 May 2017]   |  

Abstract

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. View Full-Text
Keywords: instrumented glove; wearable technology; hand dexterity assessment; IMU sensors; surgical simulation; movement signal processing instrumented glove; wearable technology; hand dexterity assessment; IMU sensors; surgical simulation; movement signal processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lemos, J.D.; Hernandez, A.M.; Soto-Romero, G. An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education. Sensors 2017, 17, 988.

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