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Minimally Invasive Surgery
Volume 2016, Article ID 5624630, 8 pages
http://dx.doi.org/10.1155/2016/5624630
Research Article

Prediction of Muscle Fatigue during Minimally Invasive Surgery Using Recurrence Quantification Analysis

Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA

Received 29 October 2015; Revised 29 February 2016; Accepted 12 April 2016

Academic Editor: Othmar Schöb

Copyright © 2016 Ali Keshavarz Panahi and Sohyung Cho. 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.

Abstract

Due to its inherent complexity such as limited work volume and degree of freedom, minimally invasive surgery (MIS) is ergonomically challenging to surgeons compared to traditional open surgery. Specifically, MIS can expose performing surgeons to excessive ergonomic risks including muscle fatigue that may lead to critical errors in surgical procedures. Therefore, detecting the vulnerable muscles and time-to-fatigue during MIS is of great importance in order to prevent these errors. The main goal of this study is to propose and test a novel measure that can be efficiently used to detect muscle fatigue. In this study, surface electromyography was used to record muscle activations of five subjects while they performed fifteen various laparoscopic operations. The muscle activation data was then reconstructed using recurrence quantification analysis (RQA) to detect possible signs of muscle fatigue on eight muscle groups (bicep, triceps, deltoid, and trapezius). The results showed that RQA detects the fatigue sign on bilateral trapezius at 47.5 minutes (average) and bilateral deltoid at 57.5 minutes after the start of operations. No sign of fatigue was detected for bicep and triceps muscles of any subject. According to the results, the proposed novel measure can be efficiently used to detect muscle fatigue and eventually improve the quality of MIS procedures with reducing errors that may result from overlooked muscle fatigue.