Wireless Communications and Mobile Computing

Volume 2017, Article ID 7318076, 17 pages

https://doi.org/10.1155/2017/7318076

## Local Parametric Approach of Wireless Capsule Endoscope Localization Using Randomly Scattered Path Loss Based WCL

Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh

Correspondence should be addressed to Umma Hany; moc.liamg@ynahammu

Received 6 July 2017; Accepted 24 October 2017; Published 4 December 2017

Academic Editor: Paolo Barsocchi

Copyright © 2017 Umma Hany and Lutfa Akter. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

We propose scattered path loss based weighted centroid localization (WCL) algorithm for wireless video capsule endoscope (VCE). The main challenge in this approach is the random deviation in the measured received signal strength indicator (RSSI) caused by multipath propagation and shadowing effects of human body channel which in turn increases the localization error. To address this issue, we propose local parameter dependent path loss representation in the training phase and apply adaptive least square error (LSE) method to extract the parameters. Then, in the test phase, we estimate distance using the extracted parameters and the randomly scattered path loss. The position of capsule is estimated using non-degree based WCL followed by a calibration process. We propose suboptimal method of estimating the calibration coefficient and also compute the optimal value of coefficient analytically to set the benchmark. We develop a simulation platform using MATLAB to present the results and to verify the performance. We gradually increase the number of sensors and place them in different topologies using different dimensions. The obtained accuracy by our proposed suboptimal method of WCL is very close to the optimal benchmark for all cases. Our proposed approach also outperforms existing works in terms of localization accuracy.

#### 1. Introduction

Wireless video capsule endoscopes (VCEs) are used to diagnose lesions along digestive tracts. For proper diagnosis, it is necessary to know the exact location of the lesions. VCE localization is a process of determining the unknown location of the capsule while it travels through the gastrointestinal (GI) tract. A group of sensors are used to localize the capsule. There are several positioning techniques in the literature [1], in general classified into range based [2–4] and range-free [5, 6]. Trilateration [2, 3] is a simple positioning technique, where the position of the mobile target is estimated by intersection of the circles centered on the fixed anchor node’s position, with a radius equal to the estimated distance between the mobile node and the anchor node. Triangulation [3, 4] uses the properties of triangles to calculate distances for position information. Centroid localization (CL) [5] is a range-free algorithm which localizes the target to the centroid of a set of reference points. Weighted centroid localization (WCL) [6] gives more weights to nearer reference node than the remote reference nodes.

The reported VCE localization algorithms in the literature [7] are based on either magnetic field or electric field strength. The method in [8] is based on magnetic field strength which reported 1.8 mm average localization error. Though the accuracy of [8] is high, the method requires more space in the capsule. Since VCE is equipped with a radio communication system, developing localization system using RF techniques got attention. In [9], the authors propose position estimation based on RFID (Radio Frequency Identification) and the antenna array. In [9], the RFID of antennas which detect the tag are measured and then coordinates of those antennas are used to estimate location applying the center of gravity location estimation algorithm. Their system does not require RF signal attenuation model as they do not need to measure RSS. They report mean estimation error of with dense antenna arrangement. In [10], the authors propose linear approximation techniques on the data received from RSSI to predict the initial position of the object using linear least square (LLS) based trilateration and based on the initial prediction, the final position of the object is computed using nonlinear least square iteration method. In [10], the exact distances between the object and the sensors and simultaneous RSSI measurements from several access points (AP) are needed for accurate estimation of position. For most of the positions, an average error of % is reported in [10]. In [11, 12], the authors use RSS triangulation and Monte Carlo simulation for multicapsule cooperative localization in the GI tract and conclude that better accuracy is possible to be achieved by increasing the number of receiver sensors. They [11] achieve average localization error of in the digestive organs for capsule endoscopy using more than 32 sensors on body surface and in [12] average localization error of is achieved using 64 sensors on body surface. In [13], the authors model the medical implant communication service (MICS) band signals using finite difference time-domain (FDTD) simulation with a numerical human model and propose maximum likelihood (ML) based position estimation using RSSI. They reported [13] 38 mm root mean square error () if the channel parameters are accurately measured whereas the RMSE may reach as high as 75 mm if the channel parameters are not accurately estimated. However, the method in [13] cannot give an accurate estimate of the capsule endoscope location with inaccurate estimated channel parameters. The authors in [14] develop a time of arrival (TOA) and path loss based one-stage method using convex optimization and spatial sparsity in space considering massive multipath and shadow fading conditions. To achieve better accuracy, their proposed system must have prior and accurate knowledge on the location of grid points and the average velocity of propagation which depends on relative permittivity of the body tissues at each grid point. As per their report [14], the method can achieve approximately RMSE with standard deviation using sensors. In [15], the authors use LLS of estimated distances and the coordinates to find initial coordinates and then nonlinear least square method is applied to find the final positions. The position estimation error reported in [15] is within for (for both homogeneous and heterogeneous phantom) and within for case for homogeneous cylinder case only. However, here the authors use the homogeneous electrical properties of muscle tissue on the simulated data from the heterogeneous phantom. For practical scenario, proper electrical properties of the nonhomogeneous tissues should be estimated and used. In [16], the nonlinear direction-of-arrival (DOA) and inertial measurement unit (IMU) measurements are integrated to track the movement of capsule using unscented Kalman filter (UKF). However, the computational complexity of their [16] proposed method is high. In [17], the authors propose hybrid method using camera motion tracking algorithm and RF localization to localize the capsule and achieve low localization error of on average. In [18], the authors propose a simple localization method using WCL and apply a calibration method using LLS of estimated and real positions. However, the method in [18] is not suitable for practical scenario as it requires prior knowledge of real positions for the calibration process. The authors in [18] report mean localization error of using eight sensors. In [19], the authors propose suboptimal degree based WCL to estimate position of capsule and report 6.27 mm error. In [18, 19], the path loss is linearized assuming minimum path loss deviation which is not realistic for human body environment. The authors in [20] propose Gaussian weighted average (GWA) based and MIMO based nonparametric methods of path loss estimation using UWB channel. The authors in [20] also propose calibrated WCL (CWCL) for VCE localization and report 5.14 mm error using MIMO based CWCL.

A major challenge in path loss based VCE localization algorithms is the modeling of human body channel which suffers severe multipath propagation and heavy shadow fading effects caused by the internal organs and nonhomogeneous medium of propagation [13, 21, 22]. The random variation in RSSI measurements and path loss estimations result in large localization error [22]. Thus, the randomness issue of channel needs to be addressed to improve the localization accuracy of VCE.

Trilateration and triangulation based VCE localization algorithms [10–12] compute the location using distance and angle information of triangle formed by three reference sensor nodes [23]. For more number of sensors, the algorithms in [10–12] require several combinations of three sensor nodes to compute the location [23] which may increase computational complexity of the localization. The performance of the algorithms in [10–12] also decreases excessively with estimation errors in channel [23]. The other methods reported in the literature [10–15] also require very precise knowledge on channel parameters or relative permittivity of the body tissues for accurate estimation of positions. As human body is a complex environment of experimentation, a simple localization approach with less computational complexity is required. WCL [6] is a localization approach [12] which can estimate location using three or more sensors with less computational complexity, less hardware cost, and less communication overhead [12, 24]. It is simple as well as robust to errors in the estimated channel model parameters [12, 23]. It has attracted a lot of interest [25–31] in outdoor environment. Though it is dependent on beacon numbers and placements, the accuracy can be significantly improved using more number of sensors [23]. Thus for a complex nonhomogeneous environment as human body, WCL is an appropriate choice for localization of VCE.

In this paper, we propose a simple localization approach for VCE using scattered path loss based WCL in presence of vast multipath propagation and shadow fading conditions of nonhomogeneous human body channel. To address the randomness issue in the measured path loss, we propose local parameter dependent path loss representation. In the training phase, we divide the traveling path of capsule into clusters of neighboring points and propose adaptive least square error (LSE) method to find local path loss parameters of the clusters. In the test phase, the local parameters and the measured path loss (randomly scattered) are used to calculate the distance of the sensors from the target. Then we calculate the weight of the sensors position using the calculated distances. Finally, the position of the capsule is estimated using WCL by finding weighted average of the sensors position. We propose suboptimal method of computing calibration coefficient to improve the accuracy of localization. We also analytically compute the optimal value of coefficient to set the benchmark of accuracy. The optimal coefficient is calculated using real positions of capsule which is not practically applicable and used only to set the benchmark of accuracy for comparison purpose. The suboptimal method calculates the coefficient using maximum distance of target and sensors which is realistic for practical implementation. We develop a simulation platform using MATLAB to show the results and to verify the accuracy of our proposed algorithm using different performance indices. We verify the accuracy by gradually increasing the number of sensors and place them following different topologies in different dimensions. We observe improved accuracy in all cases. We observe that using optimal coefficient based method, root mean square error () of 13.17 mm is obtained whereas using our proposed suboptimal method, of 10.43 mm is achievable without prior knowledge of real positions which is very close to the benchmark accuracy.

#### 2. System Overview

We consider a sensor array to localize the video endoscope capsule (VCE) equipped with a radio transmitter (Tx) as shown in Figure 1. The capsule transmitter uses a MICS band multiturn loop antenna to emit radio wave while it travels through the small intestine. The sensors are configured as RF receivers (Rx) which receive radio signals transmitted from the capsule transmitter. The cubical sensor array consists of a number of sensors (8–64) placed at the corner points which may be set on a body surrounded belt or jacket. The receivers measure the received signal strength indicator (RSSI) of signal transmitted from the VCE traveling through possible coordinate points inside the small intestine of dimension. The measured RSSI is sent to the central unit for further processing. The central unit is incorporated with the localization tool which is used to estimate the position of the capsule using our proposed algorithms. Most of the notations and symbols used in the whole paper are listed in Notations.