Calibrated analytical model for magnetic localization of wireless capsule endoscope based on onboard sensing

Li, You; Huang, Zhuokang; Liu, Xiaobo; Yu, Jie; Song, Chaoyang; Hu, Chengzhi

doi:10.1017/s0263574722001849

Cited by 4 publications

(1 citation statement)

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“…Along with locomotion, localization capability is also of great importance to monitor the spatial coordinates of the capsule [15]. Localization has been achieved based on time and direction of arrival [16], radiofrequency identification [17], and received signal strength indicator [18], through measurement of the magnetic fields of the capsule using magnetic sensors [19,20] and video localization [21].…”

Section: Introductionmentioning

confidence: 99%

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Peker,

Beşer,

Işıldar

et al. 2023

Robotica

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We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

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