Sensory-Glove-Based Open Surgery Skill Evaluation

Laura Sbernini, Lucia Rita Quitadamo, Francesco Riillo, Nicola Di Lorenzo, Achille Lucio Gaspari, Giovanni Saggio

Research output: Contribution to journalArticle

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.
Original languageEnglish
Pages (from-to)1-6
JournalIEEE Transactions on Human-Machine Systems
Early online date23 Jan 2018
DOIs
Publication statusPublished - 23 Jan 2018

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Surgery
surgery
Neural networks
neural network
Discriminant analysis
evaluation
performance
Support vector machines
Classifiers
discriminant analysis
Sensors
experience
expertise
ability

Bibliographical note

© 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.

Keywords

  • Gesture recognition
  • manual dexterity
  • motion capture
  • training evaluation
  • wearable systems

Cite this

Sbernini, L., Quitadamo, L. R., Riillo, F., Lorenzo, N. D., Gaspari, A. L., & Saggio, G. (2018). Sensory-Glove-Based Open Surgery Skill Evaluation. IEEE Transactions on Human-Machine Systems, 1-6. https://doi.org/10.1109/THMS.2017.2776603
Sbernini, Laura ; Quitadamo, Lucia Rita ; Riillo, Francesco ; Lorenzo, Nicola Di ; Gaspari, Achille Lucio ; Saggio, Giovanni. / Sensory-Glove-Based Open Surgery Skill Evaluation. In: IEEE Transactions on Human-Machine Systems. 2018 ; pp. 1-6.
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Sbernini, L, Quitadamo, LR, Riillo, F, Lorenzo, ND, Gaspari, AL & Saggio, G 2018, 'Sensory-Glove-Based Open Surgery Skill Evaluation', IEEE Transactions on Human-Machine Systems, pp. 1-6. https://doi.org/10.1109/THMS.2017.2776603

Sensory-Glove-Based Open Surgery Skill Evaluation. / Sbernini, Laura; Quitadamo, Lucia Rita; Riillo, Francesco; Lorenzo, Nicola Di; Gaspari, Achille Lucio; Saggio, Giovanni.

In: IEEE Transactions on Human-Machine Systems, 23.01.2018, p. 1-6.

Research output: Contribution to journalArticle

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AU - Saggio, Giovanni

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Sbernini L, Quitadamo LR, Riillo F, Lorenzo ND, Gaspari AL, Saggio G. Sensory-Glove-Based Open Surgery Skill Evaluation. IEEE Transactions on Human-Machine Systems. 2018 Jan 23;1-6. https://doi.org/10.1109/THMS.2017.2776603