Sensory-Glove-Based Open Surgery Skill Evaluation

Abstract

Manual dexterity is one of the most important surgical skills, and yet there are limited instruments to evaluate this ability objectively. In this paper, we propose a system designed to track surgeons’ hand movements during simulated open surgery tasks and to evaluate their manual expertise. Eighteen participants, grouped according to their surgical experience, performed repetitions of two basic surgical tasks, namely single interrupted suture and simple running suture. Subjects’ hand movements were measured with a sensory glove equipped with flex and inertial sensors, tracking flexion/extension of hand joints, and wrist movement. The participants’ level of experience was evaluated discriminating manual performances using linear discriminant analysis, support vector machines, and artificial neural network classifiers. Artificial neural networks showed the best performance, with a median error rate of 0.61% on the classification of single interrupted sutures and of 0.57% on simple running sutures. Strategies to reduce sensory glove complexity and increase its comfort did not affect system performances substantially.

Publication DOI: https://doi.org/10.1109/THMS.2017.2776603
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Gesture recognition,manual dexterity,motion capture,training evaluation,wearable systems
Publication ISSN: 2168-2305
Last Modified: 04 Mar 2024 08:25
Date Deposited: 08 Feb 2018 09:30
Full Text Link:
Related URLs: http://ieeexplo ... cument/8267323/ (Publisher URL)
PURE Output Type: Article
Published Date: 2018-04-01
Published Online Date: 2018-01-23
Accepted Date: 2017-10-25
Authors: Sbernini, Laura
Quitadamo, Lucia Rita (ORCID Profile 0000-0003-1877-4672)
Riillo, Francesco
Lorenzo, Nicola Di
Gaspari, Achille Lucio
Saggio, Giovanni

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