Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator

In this study, we propose a novel endoscopic manipulation system that is controlled by a surgeon's eye movements. The optical axis direction of the endoscopic manipulator is altered intuitively based on the surgeon's pupil movements. A graphical user interface was developed by divi...

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Autores principales:	Yang CAO, Satoshi MIURA, Quanquan LIU, Yo KOBAYASHI, Kazuya KAWAMURA, Shigeki SUGANO, Masakatsu G. FUJIE
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2017
Materias:	laparoscopic surgery endoscopic manipulator master-slave structure eye tracking calibration probabilistic neural network Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/a70db1c778b04fbdac1e0ffdf463ae03
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id	oai:doaj.org-article:a70db1c778b04fbdac1e0ffdf463ae03
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spelling	oai:doaj.org-article:a70db1c778b04fbdac1e0ffdf463ae032021-11-26T07:06:29ZProbabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator2187-974510.1299/mej.15-00568https://doaj.org/article/a70db1c778b04fbdac1e0ffdf463ae032017-07-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/4/4/4_15-00568/_pdf/-char/enhttps://doaj.org/toc/2187-9745In this study, we propose a novel endoscopic manipulation system that is controlled by a surgeon's eye movements. The optical axis direction of the endoscopic manipulator is altered intuitively based on the surgeon's pupil movements. A graphical user interface was developed by dividing the monitor screen into several areas with shape boundaries, so that the movement direction of the endoscope can be identified by the area gazed at by the operator. We used a probabilistic neural network (PNN) to decide the regional distribution proportion to recognize the direction in which the operator would want the endoscopic manipulator to move. The PNN model was trained by individual calibration data. We hypothesized that PNN model training could be completed immediately after calibration, which also determines the boundary of the regional distribution portion (RDP). We designed an experiment and recorded the path of direction change to verify the PNN's effectiveness in our proposed system. All participants, including four who wore glasses, completed the requested task. Moreover, wearing glasses had no significant effect on the performance of the proposed system. Furthermore, the PNN training duration only took 2% of the entire time of the procedure to handle individual differences. We conclude that our method can handle individual differences in operators' eyes through machine learning of personal calibration data over a short time frame, which will not take significant extra preoperative setup time.Yang CAOSatoshi MIURAQuanquan LIUYo KOBAYASHIKazuya KAWAMURAShigeki SUGANOMasakatsu G. FUJIEThe Japan Society of Mechanical Engineersarticlelaparoscopic surgeryendoscopic manipulatormaster-slave structureeye trackingcalibrationprobabilistic neural networkMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 4, Iss 4, Pp 15-00568-15-00568 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	laparoscopic surgery endoscopic manipulator master-slave structure eye tracking calibration probabilistic neural network Mechanical engineering and machinery TJ1-1570
spellingShingle	laparoscopic surgery endoscopic manipulator master-slave structure eye tracking calibration probabilistic neural network Mechanical engineering and machinery TJ1-1570 Yang CAO Satoshi MIURA Quanquan LIU Yo KOBAYASHI Kazuya KAWAMURA Shigeki SUGANO Masakatsu G. FUJIE Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
description	In this study, we propose a novel endoscopic manipulation system that is controlled by a surgeon's eye movements. The optical axis direction of the endoscopic manipulator is altered intuitively based on the surgeon's pupil movements. A graphical user interface was developed by dividing the monitor screen into several areas with shape boundaries, so that the movement direction of the endoscope can be identified by the area gazed at by the operator. We used a probabilistic neural network (PNN) to decide the regional distribution proportion to recognize the direction in which the operator would want the endoscopic manipulator to move. The PNN model was trained by individual calibration data. We hypothesized that PNN model training could be completed immediately after calibration, which also determines the boundary of the regional distribution portion (RDP). We designed an experiment and recorded the path of direction change to verify the PNN's effectiveness in our proposed system. All participants, including four who wore glasses, completed the requested task. Moreover, wearing glasses had no significant effect on the performance of the proposed system. Furthermore, the PNN training duration only took 2% of the entire time of the procedure to handle individual differences. We conclude that our method can handle individual differences in operators' eyes through machine learning of personal calibration data over a short time frame, which will not take significant extra preoperative setup time.
format	article
author	Yang CAO Satoshi MIURA Quanquan LIU Yo KOBAYASHI Kazuya KAWAMURA Shigeki SUGANO Masakatsu G. FUJIE
author_facet	Yang CAO Satoshi MIURA Quanquan LIU Yo KOBAYASHI Kazuya KAWAMURA Shigeki SUGANO Masakatsu G. FUJIE
author_sort	Yang CAO
title	Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
title_short	Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
title_full	Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
title_fullStr	Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
title_full_unstemmed	Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
title_sort	probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator
publisher	The Japan Society of Mechanical Engineers
publishDate	2017
url	https://doaj.org/article/a70db1c778b04fbdac1e0ffdf463ae03
work_keys_str_mv	AT yangcao probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT satoshimiura probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT quanquanliu probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT yokobayashi probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT kazuyakawamura probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT shigekisugano probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator AT masakatsugfujie probabilisticneuralnetworkappliedtoeyetrackingcontroltoalterthedirectionofanendoscopicmanipulator
_version_	1718409737578479616

Probabilistic neural network applied to eye tracking control to alter the direction of an endoscopic manipulator

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