Surgical Gesture Recognition in Robot-Assisted Surgery Using Machine Learning Methods on Kinematic Data.
This work focuses on training machine learning models to recognize gestures during robot-assisted surgical procedures in real-time, using exclusively kinematic data from the patient-side manipulators. The JIGSAWS dataset, specifically the suturing tasks, serves as the evaluation benchmark. We experi...
| Published in: | Studies in Health Technology & Informatics Vol. 330; pp. 568 - 594 |
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| Main Authors: | , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193074364&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193074364 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 330 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 193074364 193074364 193074364 10.3233/SHTI251451 193074364 ppf: 568 ppct: 26 formats: tig: atl: Surgical Gesture Recognition in Robot-Assisted Surgery Using Machine Learning Methods on Kinematic Data. aug: au: DIMITRIADIS, Alexandros MOUSTRIS, George TZAFESTAS, Costas affil: National Technical University of Athens (NTUA), School of Electrical and Computer Engineering. sug: subj: Robotic Surgical Procedures Machine Learning Methods Body Language Evaluation Kinematics Evaluation Human Descriptive Statistics Automation Deep Learning Neural Networks (Computer) Recurrent Neural Networks Dimensionality Reduction Minimally Invasive Procedures Convolutional Neural Networks ab: This work focuses on training machine learning models to recognize gestures during robot-assisted surgical procedures in real-time, using exclusively kinematic data from the patient-side manipulators. The JIGSAWS dataset, specifically the suturing tasks, serves as the evaluation benchmark. We experimented with various neural network architectures, using an LSTM architecture as the baseline approach. To further enhance performance, two hybrid approaches are proposed in this work: the first one combining an LSTM with a Conditional Random Field (CRF) and the second one integrating an attention layer. An extensive experimental study was conducted to evaluate and optimize the performance of the different approaches, and identify areas for improvement. A thorough comparative analysis of the results shows that the proposed hybrid approaches, in particular the one combining an attention layer, can improve recognition rate, as compared to relevant state-of-the-art, with accuracy of 81.56%. This study lays the foundation for further research in the field, focusing on advancing real-time surgical gesture recognition as a means to develop tools that can provide intraoperative monitoring and assistance to the surgeon. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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