Design and control of a magnetically driven capsule-robot for endoscopy and drug delivery

Hosseini, Saman; Khamesee, Mir Behrad

doi:10.1109/tic-sth.2009.5444409

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Since the biomedical sensor moves together with its magnet, magnetic-signal-based methods have the advantages of magnetic levitation, robotic magnetic steering, helical propulsion by a rotational magnetic field and remote magnetic manipulation [67,68,69]. Hence, a considerable amount of research is focused on building active locomotion biomedical sensors and their settings [34,44,70]. The main advantage of positioning techniques based on the magnetic field strength is that low-frequency magnetic fields can run through the body with reduced attenuation since tissues of the human body are non-magnetic.…”

Section: Magnetic-signal-based Localization and Trackingmentioning

confidence: 99%

“…Most researchers focus on the use of a permanent magnet inside the WBS in the literature, since this approach provides the generation of a magnetic field, and based on the magnet position and orientation, magnetic sensors placed outside the patient’s body can detect the magnetic flux intensities [6,7,34,39]. The magnet position and orientation can be computed by feeding the sensor data to an appropriate algorithm [37,38] based on the well-established mathematical model of the magnetic field of a magnet with position

{[a, b, c]}^{T}

and orientation

H_{0} = {[m, n, p]}^{T}

, at a certain point

{[x, y, z]}^{T}

, given by:

B = \frac{μ_{0} μ_{T} M_{T}}{4 π} (() \frac{3 (H_{0}^{T} P) P}{{∥ P ∥}^{5}} - \frac{H_{0}}{{∥ P ∥}^{3}}),

M_{T} = π σ^{2} L M_{0},

where

P = {[x - a, y - b, z - c]}^{T}

is the relative position of the magnet.…”

Section: Magnetic-signal-based Localization and Trackingmentioning

confidence: 99%

“…Accordingly, many research groups are investigating the design and development of active locomotion sensors and settings [7,32,33,34]. …”

Section: Magnetic-signal-based Localization and Trackingmentioning

confidence: 99%

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Localization and Tracking of Implantable Biomedical Sensors

Umay

Fi̇dan

Barshan

2017

Sensors

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Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.

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Section: Magnetic-signal-based Localization and Trackingmentioning

confidence: 99%

{[a, b, c]}^{T}

and orientation

H_{0} = {[m, n, p]}^{T}

, at a certain point

{[x, y, z]}^{T}

, given by:

B = \frac{μ_{0} μ_{T} M_{T}}{4 π} (() \frac{3 (H_{0}^{T} P) P}{{∥ P ∥}^{5}} - \frac{H_{0}}{{∥ P ∥}^{3}}),

M_{T} = π σ^{2} L M_{0},

where

P = {[x - a, y - b, z - c]}^{T}

is the relative position of the magnet.…”

Section: Magnetic-signal-based Localization and Trackingmentioning

confidence: 99%

See 1 more Smart Citation

Localization and Tracking of Implantable Biomedical Sensors

Umay

Fi̇dan

Barshan

2017

Sensors

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“…These methods provide much more accurate visualization gastrointestinal diseases than could be achieved in earlier endoscope systems, enabling physicians to more accurately diagnose patient ailments. Endoscopy greatly improves the quality of patient care by enabling minimally invasive surgical techniques [7]. While traditional surgeries required large incisions to enable surgeons to view the subject tissue and to use large, hand-held instruments, endoscopes and laparoscopes (a type of endoscope with a rigid tube) [8] enable minimally invasive surgical techniques with only one or two incisions less than a centimeter in length.…”

Section: Fig-1(b) Block Diagram Of Endoscopy Capsulementioning

confidence: 99%

A Methodology for Very Low Power and Small Size Capsule Manufacturing for Wireless Capsule Endoscopy

Rathore¹,

Tiwary²,

Kumar³

2014

IJCA

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We are presenting a method of reducing the size of traditional capsule used in wireless capsule endoscopy (WCE) for. The conventional wireless endoscopic capsule was manufactured on 18µm technology and its size is 26mmx11mm. Here we are using Ultrascale FinFET 16nm technology to reduce the size of capsule. By using this technique the Size of traditional capsule can be reduced by 11-12 % .Using this method leakage current content reduces and power consumption also reduces up to 50% than the traditional one. The device can be operated on a very low operating voltage and thus the life of battery increases. But after the advancement of FPGA technology, here we are emphasizing on ultra-scale technology with 16nm FINFET technology for fabrication of CMOS transistors using 16 nm technology 3D ICs. This method increases system performance and speed upto 2 folds and speed increases upto 30% .

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“…Hence, we focus on algorithms with very low complexity. This will give the opportunity to add additional features (e.g., robotic capability, speed control, [17][18][19][20], etc.) with the available capsules.…”

Section: Design Criterionmentioning

confidence: 99%

Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

Khan

Wahid

2011

VLSI Design

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We present a lossless and low-complexity image compression algorithm for endoscopic images. The algorithm consists of a static prediction scheme and a combination of golomb-rice and unary encoding. It does not require any buffer memory and is suitable to work with any commercial low-power image sensors that output image pixels in raster-scan fashion. The proposed lossless algorithm has compression ratio of approximately 73% for endoscopic images. Compared to the existing lossless compression standard such as JPEG-LS, the proposed scheme has better compression ratio, lower computational complexity, and lesser memory requirement. The algorithm is implemented in a 0.18 μm CMOS technology and consumes 0.16 mm × 0.16 mm silicon area and 18 μW of power when working at 2 frames per second.

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Design and control of a magnetically driven capsule-robot for endoscopy and drug delivery

Cited by 13 publications

References 8 publications

Localization and Tracking of Implantable Biomedical Sensors

Localization and Tracking of Implantable Biomedical Sensors

A Methodology for Very Low Power and Small Size Capsule Manufacturing for Wireless Capsule Endoscopy

Lossless and Low-Power Image Compressor for Wireless Capsule Endoscopy

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