Endoluminal Motion Recognition of a Magnetically-Guided Capsule Endoscope Based on Capsule-Tissue Interaction Force.

flexible force sensors magnetically-guided capsule endoscope motion recognition robotic colonoscopy

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
30 Mar 2021
Historique:
received: 28 01 2021
revised: 18 03 2021
accepted: 29 03 2021
entrez: 3 4 2021
pubmed: 4 4 2021
medline: 28 4 2021
Statut: epublish

Résumé

A magnetically-guided capsule endoscope, embedding flexible force sensors, is designed to measure the capsule-tissue interaction force. The flexible force sensor is composed of eight force-sensitive elements surrounding the internal permanent magnet (IPM). The control of interaction force acting on the intestinal wall can reduce patient's discomfort and maintain the magnetic coupling between the external permanent magnet (EPM) and the IPM during capsule navigation. A flexible force sensor can achieve this control. In particular, by analyzing the signals of the force sensitive elements, we propose a method to recognize the status of the motion of the magnetic capsule, and provide corresponding formulas to evaluate whether the magnetic capsule follows the motion of the external driving magnet. Accuracy of the motion recognition in Ex Vivo tests reached 94% when the EPM was translated along the longitudinal axis. In addition, a method is proposed to realign the EPM and the IPM before the loss of their magnetic coupling. Its translational error, rotational error, and runtime are 7.04 ± 0.71 mm, 3.13 ± 0.47∘, and 11.4 ± 0.39 s, respectively. Finally, a control strategy is proposed to prevent the magnetic capsule endoscope from losing control during the magnetically-guided capsule colonoscopy.

Identifiants

pubmed: 33808443
pii: s21072395
doi: 10.3390/s21072395
pmc: PMC8036640
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Key R&D Program of China
ID : 2019YFB1312400

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Auteurs

Peisen Zhang (P)

Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Jing Li (J)

School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100081, China.
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.

Weimin Zhang (W)

Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.

Yang Hao (Y)

Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Gastone Ciuti (G)

Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.
The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.

Tatsuo Arai (T)

Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.

Paolo Dario (P)

Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.
The Biorobotics Institute, Scuola Superiore Sant'Anna, 56025 Pisa, Italy.

Qiang Huang (Q)

Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.
Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China.

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Classifications MeSH