Localization for capsule endoscopy at UWB frequencies using an experimental multilayer phantom

[EN] Localization inside the human body using ultrawideband (UWB) wireless technology is gaining importance in several medical applications such as capsule endoscopy. Performance analysis of RF based localization techniques are mainly conducted through simulations using numerical human models or through experimental measurements using homogeneous phantoms. One of the most common implemented RF localization approaches uses the received signal strength (RSS). However, to the best of our knowledge, no experimental measurements employing multilayer phantoms are currently available in literature. This paper investigates the performance of RSS-based technique for two-dimensional (2D) localization by employing a two-layer experimental phantom-based setup. Preliminary results on the estimation of the in-body antenna coordinates show that RSS-based method can achieve a location accuracy on average of 0.5-1 cm within a certain range of distances between in-body and on-body antenna.This work was supported by the European Union’s H2020:MSCA:ITN program for the ”Wireless In-body Environment Communication- WiBEC” project under the grant agreement no. 675353. This work was also funded by the Programa de Ayudas de Investigación y Desarrollo (PAID-01-16) from Universitat Politècnica de València and by the Ministerio de Economía y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Barbi, M.; Pérez Simbor, S.; García Pardo, C.; Andreu Estellés, C.; Cardona Marcet, N. (2018). Localization for capsule endoscopy at UWB frequencies using an experimental multilayer phantom. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WCNCW.2018.8369015

Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization

[EN] In recent years, localization for capsule endoscopy applications using Ultra-Wideband (UWB) technology has become an attractive field of study due to its potential benefits for patients. Performance analysis of RF-based localization techniques are very limited in literature. Most of the available studies rely on software simulations using digital human models. Nonetheless, no realistic studies based on in-vivo measurements has been reported yet. This paper investigates the performance of RSS-based technique for three-dimensional (3D) localization in the UWB frequency band. Impact of receivers selection as well as of the evaluated path loss model on the localization accuracy is investigated. Results obtained through CST-based simulations and from recently conducted in-vivo measurements are presented and compared.This work was supported by the European Union's H2020:MSCA:ITN program for the "Wireless In-body Environment Communication- WiBEC" project under the grant agreement no. 675353. This work was also funded by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Barbi, M.; Garcia-Pardo, C.; Cardona Marcet, N.; Andrea Nevárez; Vicente Pons Beltrán; Frasson, M. (2018). Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization. IEEE. 340-344. https://doi.org/10.1109/PIMRC.2018.8580862S34034

Analysis of the Localization Error for Capsule Endoscopy Applications at UWB Frequencies

Localization for Wireless Capsule Endoscopy (WCE) in the Ultra-Wideband frequency band is a very active field of investigation due to its potential advantages in future endoscopy applications. Received Signal Strength (RSS) based localization is commonly preferred due to its simplicity. Previous studies on Ultra-Wideband (UWB) RSS-based localization showed that the localization accuracy depends on the average ranging error related to the selected combination of receivers, which not always is the one experiencing the highest level of received power. In this paper the tendency of the localization error is further investigated through supplementary software simulations and previously conducted laboratory measurements. Two-dimensional (2D) and three-dimensional (3D) positioning are performed and the trend of the localization error compared in both cases. Results shows that the distribution of the selected path loss values, corresponding to the receivers used for localization, around the in-body position to estimate also affects the localization accuracy.This work was supported by the H2020:MSCA:ITN program for the “Wireless In-body Environment Communication- WiBEC” project under the grant agreement no. 675353. This work was also supported by the European Union’s H2020:MSCA:ITN program for the ”mmWave Communications in the Built Environments - WaveComBE” project under the grant agreement no. 766231.Barbi, M.; Pérez-Simbor, S.; Garcia-Pardo, C.; Cardona Marcet, N. (2019). Analysis of the Localization Error for Capsule Endoscopy Applications at UWB Frequencies. IEEE. https://doi.org/10.1109/ISMICT.2019.8743813

A hybrid localization method for Wireless Capsule Endoscopy (WCE)

Wireless capsule endoscopy (WCE) is a well-established diagnostic tool for visualizing the Gastrointestinal (GI) tract. WCE provides a unique view of the GI system with minimum discomfort for patients. Doctors can determine the type and severity of abnormality by analyzing the taken images. Early diagnosis helps them act and treat the disease in its earlier stages. However, the location information is missing in the frames. Pictures labeled by their location assist doctors in prescribing suitable medicines. The disease progress can be monitored, and the best treatment can be advised for the patients. Furthermore, at the time of surgery, it indicates the correct position for operation. Several attempts have been performed to localize the WCE accurately. Methods such as Radio frequency (RF), magnetic, image processing, ultrasound, and radiative imaging techniques have been investigated. Each one has its strengths and weaknesses. RF-based and magnetic-based localization methods need an external reference point, such as a belt or box around the patient, which limits their activities and causes discomfort. Computing the location solely based on an external reference could not distinguish between GI motion from capsule motion. Hence, this relative motion causes errors in position estimation. The GI system can move inside the body, while the capsule is stationary inside the intestine. This proposal presents two pose fusion methods, Method 1 and Method 2, that compensate for the relative motion of the GI tract with respect to the body. Method 1 is based on the data fusion from the Inertial measurement unit (IMU) sensor and side wall cameras. The IMU sensor consists of 9 Degree-Of-Freedom (DOF), including a gyroscope, an accelerometer, and a magnetometer to monitor the capsule’s orientation and its heading vector (the heading vector is a three-dimensional vector pointing to the direction of the capsule's head). Four monochromic cameras are placed at the side of the capsule to measure the displacement. The proposed method computes the heading vector using IMU data. Then, the heading vector is fused with displacements to estimate the 3D trajectory. This method has high accuracy in the short term. Meanwhile, due to the accumulation of errors from side wall cameras, the estimated trajectory tends to drift over time. Method 2 was developed to resolve the drifting issue while keeping the same positioning error. The capsule is equipped with four side wall cameras and a magnet. Magnetic localization acquires the capsule’s global position using 9 three-axis Hall effect sensors. However, magnetic localization alone cannot distinguish between the capsule’s and GI tract’s motions. To overcome this issue and increase tracking accuracy, side wall cameras are utilized, which are responsible for measuring the capsule’s movement, not the involuntary motion of the GI system. A complete setup is designed to test the capsule and perform the experiments. The results show that Method 2 has an average position error of only 3.5 mm and can compensate for the GI tract’s relative movements. Furthermore, environmental parameters such as magnetic interference and the nonhomogeneous structure of the GI tract have little influence on our system compared to the available magnetic localization methods. The experiment showed that Method 2 is suitable for localizing the WCE inside the body

Localization and Tracking of Intestinal Paths for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) is a non-invasive technology used for visual inspection of the human gastrointestinal (GI) tract. Localization of the capsule is a vital component of the system, as this enables physicians to identify the position of abnormalities. Several approaches exist that use the received signal strength (RSS) of the radio frequency (RF) signals for localization. However, few of these utilize the sparseness of the signals. Due to intestinal motility, the capsule positions will change with time. The distance travelled by the capsule in the intestine, however, remains more or less constant with time. In this thesis, a compressive sensing (CS) based localization algorithm is presented, that utilize signal sparsity in the RSS measurements. Different L1-minimization algorithms are used to find the sparse location vector. The performance is evaluated by electromagnetic (EM) simulations performed on a human voxel model, using narrow-band (NB) and ultra wide-band (UWB) signals. From intestinal positions, the distance the capsule has travelled is estimated by use of Kalman- and particle filters. It was found that localization accuracy of a few millimeters is possible under ideal conditions, when the RSS measurements are generated from a path loss model. When using path loss data from the EM simulations, localization accuracy on the order of 20-30 mm was achievable for NB signals. Use of UWB signals resulted in localization errors between 35-60 mm, depending on frequency range and bandwidth. From generated intestinal positions, the travelled distance was estimated with a minimum accuracy of a few millimeters, when using a VNL Kalman filter and moderate amounts of observation noise. The results are found from a limited amount of data. In order to increase the confidence in the presented results, the performance of the localization algorithm and the filters should be evaluated with a larger number of datasets