"Precise and accurate localization and motion classification is an emerging fundamental areas for scientific research and engineering developments. Such science and technology began from the broad out door area applications, and gradually grew into smaller and more complicated in-door area and more recently it is proceeding into in-body area networking for medical applications. Localization and motion classification technologies have their own specific challenges depending on the application and environment, which are left for scientists and engineers to overcome. One major challenge is that location estimation and motion classification often use hand-held devices or wearable sensors. Such devices and sensors usually work in indoor, near body environments and the human object has certain effects on the measurements. In that situation, existing mathematical models for general environments are no longer accurate and new models and analytical approaches are required to deal with the human body effects. This has opened opportunities for researchers to tackle a number of demanding problems. This dissertation focuses on three novel problems in localization and motion classification using radio propagation (RF) modeling, in and around the human body. (1) We develop an empirical Time-of-Arrival (TOA) ranging error model for radio propagation from body-mounted sensors to external access points, for human body tracking in indoor environment. This model reflects the effects of human angular motion on TOA ranging estimation, which enables accurate analysis for conventional TOA-based human tracking systems. (2) We use empirical data collected from a RF connection between a pair of body-mounted sensors to classify seven frequently appeared human body motions. This RF based classification approach has enabled health monitoring applications for first responders, hospital patient, and elderly care centers and in most of the situations it can replace the costly video base monitoring systems. (3) We use radio propagation models from body-mounted sensor to medical implants and the moving pattern of micro-robots inside the body to analyze the accuracy of hybrid localization inside the human body. This analysis demonstrates the feasibility of millimeter level of accurate localization inside the human body, which opens up possibilities for 3D reconstruction of the interior of human GI tract."

In Time-of-Arrival (TOA) based indoor human tracking system, the human body mounted with the target sensor can cause non-line-of-sight (NLOS) scenario and result in significant ranging error. In this thesis, we measured the TOA ranging error in a typical indoor environment and analyzed sources of inaccuracy in TOAbased indoor localization system. To quantitatively describe the TOA ranging error caused by human body, we introduce a statistical TOA ranging error model for body mounted sensors based on the measurement results. This model separates the ranging error into multipath error and NLOS error caused by the on-body creeping wave phenomenon. Both multipath error and NLOS error are modeled as a Gaussian variable. The distribution of multipath error is only relative to the bandwidth of the system while the distribution of NLOS error is relative to the angle between human facing direction and the direction of Transmitter-Receiver, signal to noise ratio (SNR) and bandwidth of the system, which clearly shows the effects of human body on TOA ranging. An efficient way to fight against the TOA ranging error caused by human body is to employ site-specific channel models by using ray-tracing technology. However, existing ray-tracing softwares lack the propagation model that takes the effects of human body into account. To address that issue, this thesis presents a empirical model for near human body ultra-wideband (UWB) propagation channel that is valid for the frequency range from 3GHz to 8GHz. It is based on measurements conducted in a anechoic chamber which can be regarded as free space. The empirical model shows the joint propagation characteristics of the on body channel and the channel between body surface and external access point. It includes the loss of the first path, arrival time of the first path and the total pathloss. Models for all three aspects have been partitioned into two sections by a break point due to the geometrical property of human body and the creeping wave phenomenon. The investigation on first path behavior can be regarded as a theoretical basis of raytracing technique that takes the effects of human body into consideration.

This research is focused on identifying an appropriate design for a reconfigurable Body Area Network (BAN). In order to investigate the benefits and drawbacks of the proposed design, a BAN system prototype was built. This system consists of two distinct node types: a slave node and a master node. These nodes communicate using ZigBee radio transceivers. The microcontroller-based slave node acquires sensor data and transmits digitized samples to the master node. The master node is FPGA-based and runs uClinux on a soft-core microcontroller. The purpose of the master node is to receive, process and store digitized sensor data. In order to verify the operation of the BAN system prototype and demonstrate reconfigurability, a specific application was required. Pattern recognition in electrocardiogram (ECG) data was the application used in this work and the MIT-BIH Arrhythmia Database was used as the known data source for verification. A custom test platform was designed and built for the purpose of injecting data from the MIT-BIH Arrhythmia Database into the BAN system. The BAN system designed and built in this work demonstrates the ability to record raw ECG data, detect R-peaks, calculate and record R-R intervals, detect premature ventricular and atrial contractions. As this thesis will identify, many aspects of this BAN system were designed to be highly reconfigurable allowing it to be used for a wide range of BAN applications, in addition to pattern recognition of ECG data.

"Body area networks (BAN) is a technology gaining widespread attention for application in medical examination, monitoring and emergency therapy. The basic concept of BAN is monitoring a set of sensors on or inside the human body which enable transfer of vital parameters between the patient´s location and the physician in charge. As body area network has certain characteristics, which impose new demands on performance evaluation of systems for wireless access and localization for medical sensors. However, real-time performance evaluation and localization in wireless body area networks is extremely challenging due to the unfeasibility of experimenting with actual devices inside the human body. Thus, we see a need for a real-time hardware platform, and this thesis addressed this need. In this thesis, we introduced a unique hardware platform for performance evaluation of body area wireless access and in-body localization. This hardware platform utilizes a wideband multipath channel simulator, the Elektrobit PROPSimâ„¢ C8, and a typical medical implantable device, the Zarlink ZL70101 Advanced Development Kit. For simulation of BAN channels, we adopt the channel model defined for the Medical Implant Communication Service (MICS) band. Packet Reception Rate (PRR) is analyzed as the criteria to evaluate the performance of wireless access. Several body area propagation scenarios simulated using this hardware platform are validated, compared and analyzed. We show that among three modulations, two forms of 2FSK and 4FSK. The one with lowest raw data rate achieves best PRR, in other word, best wireless access performance. We also show that the channel model inside the human body predicts better wireless access performance than through the human body. For in-body localization, we focus on a Received Signal Strength (RSS) based localization algorithm. An improved maximum likelihood algorithm is introduced and applied. A number of points along the propagation path in the small intestine are studied and compared. Localization error is analyzed for different sensor positions. We also compared our error result with the Cramèr- Rao lower bound (CRLB), shows that our localization algorithm has acceptable performance. We evaluate multiple medical sensors as device under test with our hardware platform, yielding satisfactory localization performance."

Body Area Networks (BANs) are emerging as a convenient option for patient monitoring. They have shown potential in improving health care services through a network of external or implanted biosensors and actuators collecting real-time physiological data. Advancements in wireless networking and sensor development are expediting the adoption of BANs. However, real-time patient monitoring still remains a challenge due to network failures and congestion. In order to improve channel loss resilience and thus link availability, a multi-radio systems approach is adopted incorporating Bluetooth and Wi-Fi. In this work, we propose a multi-radio interface designed for a BAN to improve end-to-end communications. A multi-radio BAN controller is introduced to interface between the two wireless protocols (Wi-Fi and Bluetooth), control inter-radio handovers, manage a shared transmission buffer, and overall, route data accordingly through the protocol stacks. Simulations are conducted to study the performance of the system by adjusting handover timing and its effect on link availability. Advancing a handover has the benefit of a higher throughput at the cost of an increase in power consumption and timing overhead. Furthermore, various human mobility models, AP placement arrangements, and network densities are simulated to evaluate the performance of the BAN multi-radio interface. Sparse networks were found to have the most gain from the addition of the secondary Bluetooth radio system, as primary AP coverage was already very limited. Simulation results for various combinations of simulation parameters are presented to illustrate the improvement in BAN dependability through a multi-radio interface.

Body Area Networks (BANs) are emerging as a convenient option for patient monitoring. They have shown potential in improving health care services through a network of external or implanted biosensors and actuators collecting real-time physiological data. Advancements in wireless networking and sensor development are expediting the adoption of BANs. However, real-time patient monitoring still remains a challenge due to network failures and congestion. In order to improve channel loss resilience and thus link availability, a multi-radio systems approach is adopted incorporating Bluetooth and Wi-Fi. In this work, we propose a multi-radio interface designed for a BAN to improve end-to-end communications. A multi-radio BAN controller is introduced to interface between the two wireless protocols (Wi-Fi and Bluetooth), control inter-radio handovers, manage a shared transmission buffer, and overall, route data accordingly through the protocol stacks. Simulations are conducted to study the performance of the system by adjusting handover timing and its effect on link availability. Advancing a handover has the benefit of a higher throughput at the cost of an increase in power consumption and timing overhead. Furthermore, various human mobility models, AP placement arrangements, and network densities are simulated to evaluate the performance of the BAN multi-radio interface. Sparse networks were found to have the most gain from the addition of the secondary Bluetooth radio system, as primary AP coverage was already very limited. Simulation results for various combinations of simulation parameters are presented to illustrate the improvement in BAN dependability through a multi-radio interface.

Wireless video capsule endoscopy (VCE) plays an increasingly important role in assisting clinical diagnoses of gastrointestinal (GI) diseases. It provides a non-invasive way to examine the entire small intestine, where other conventional endoscopic instruments can barely reach. Existing examination systems for the VCE cannot track the location of a endoscopic capsule, which prevents the physician from identifying the exact location of the diseases. During the eight hour examination time, the video capsule continuously keeps taking images at a frame rate up to six frame per sec, so it is possible to extract the motion information from the content of the image sequence. Many attempts have been made to develop computer vision algorithms to detect the motion of the capsule based on the small changes in the consecutive video frames and then trace the location of the capsule. However, validation of those algorithms has become a challenging topic because conducting experiments on the human body is extremely difficult due to individual differences and legal issues. In this thesis, two validation approaches for motion tracking of the VCE are presented in detail respectively. One approach is to build a physical testbed with a plastic pipe and an endoscopy camera; the other is to build a virtual testbed by creating a three-dimensional virtual small intestine model and simulating the motion of the capsule. Based on the virtual testbed, a physiological factor, intestinal contraction, has been studied in terms of its influence on visual based localization algorithm and a geometric model for measuring the amount of contraction is proposed and validated via the virtual testbed. Empirical results have made contributions in support of the performance evaluation of other research on the visual based localization algorithm of VCE.

"Localization inside the human body using Radio Frequency (RF) transmission is gaining importance in a number of applications such as Wireless Capsule Endoscopy. The accuracy of RF localization depends on the technology adopted for this purpose. The two most common RF localization technologies use Received Signal Strength (RSS) and Time-Of-Arrival (TOA). This research first provides bounds for accuracy of localization of a Endoscopy capsule inside the human body as it moves through the gastro-Intestinal track with and without randomness in transmit power using RSS based localization with a triangulation algorithm. It is observed that in spite of presence of a large number of anchor nodes; the localization error is still in range of few cm, which is quite high; hence we resort to TOA based localization. Due to lack of a widely accepted model for TOA based localization inside human body we use a computational technique for simulation inside and around the human body, named Finite Difference Time Domain (FDTD). We first show that our proprietary FDTD simulation software shows acceptable results when compared with real empirical measurements using a vector network analyzer. We then show that, the FDTD method, which has been used extensively in all kinds of electromagnetic modeling due to its versatility and simplicity, suffers seriously because of its demanding requirement on memory storage and computation time, which is due to its inherently recursive nature and the need for absorbing boundary conditions. In this research we suggest a novel computationally efficient technique for simulation using FDTD by considering FDTD as a Linear Time Invariant (LTI) system. Then we use the software to simulate the TOA of the narrowband and wideband signals propagated inside the human body for RF localization to compare the accuracies of the two using this method. "

In this thesis, a novel graphene (GN) based electrocardiogram (ECG) sensor is designed, constructed and tested to validate the concept of coating GN, which is a highly electrically conductive material, on Ag substrates of conventional electrodes. The background theory, design, experiments and results for the proposed GN-based ECG sensor are also presented. Due to the attractive electrical and physical characteristics of graphene, a new ECG sensor was investigated by coating GN onto itself. The main focus of this project was to examine the effect of GN on ECG monitoring and to compare its performance with conventional methods. A thorough investigation into GN synthesis on Ag substrate was conducted, which was accompanied by extensive simulation and experimentation. A GN-enabled ECG electrode was characterised by Raman spectroscopy, scanning electron microscopy along with electrical resistivity and conductivity measurements. The results obtained from the GN characteristic experimentation on Raman spectroscopy, detected a 2D peak in the GN-coated electrode, which was not observed with the conventional Ag/AgCl electrode. SEM characterisation also revealed that a GN coating smooths the surface of the electrode and hence, improves the skin-to-electrode contact. Furthermore, a comparison regarding the electrical conductivity calculation was made between the proposed GN-coated electrodes and conventional Ag/AgCl ones. The resistance values obtained were 212.4 Ω and 28.3 Ω for bare and GN-coated electrodes, respectively. That indicates that the electrical conductivity of GN-based electrodes is superior and hence, it is concluded that skin-electrode contact impedance can be lowered by their usage. Additional COMSOL simulation was carried out to observe the effect of an electrical field and surface charge density using GN-coated and conventional Ag/AgCl electrodes on a simplified human skin model. The results demonstrated the effectiveness of the addition of electrical field and surface charge capabilities and hence, coating GN on Ag substrates was validated through this simulation. This novel ECG electrode was tested with various types of electrodes on ten different subjects in order to analyse the obtained ECG signals. The experimental results clearly showed that the proposed GN-based electrode exhibits the best performance in terms of ECG signal quality, detection of critical waves of ECG morphology (P-wave, QRS complex and T-wave), signal-to-noise ratio (SNR) with 27.0 dB and skin-electrode contact impedance (65.82 kΩ at 20 Hz) when compared to those obtained by conventional a Ag/AgCl electrode. Moreover, this proposed GN-based ECG sensor was integrated with core body temperature (CBT) sensor in an ear-based device, which was designed and printed using 3D technology. Subsequently, a finger clipped photoplethysmography (PPG) sensor was integrated with the two-sensors in an Arduino based data acquisition system, which was placed on the subject's arm to enable a wearable multiple physiological measurement system. The physiological information of ECG and CBT was obtained from the ear of the subject, whilst the PPG signal was acquired from the finger. Furthermore, this multiple physiological signal was wirelessly transmitted to the smartphone to achieve continuous and real-time monitoring of physiological signals (ECG, CBT and PPG) on a dedicated app developed using the Java programming language. The proposed system has plenty of room for performance improvement and future development will make it adaptabadaptable, hence being more convenient for the users to implement other applications than at present.

Les progrès récents dans les technologies et les systèmes de communications sans fil soutenus par la miniaturisation de dispositifs ont donné naissance une nouvelle génération de réseaux personnels permettant des communications autour du corps humain: les réseaux corporels. Cette thèse étudie les différents types du canal de propagation des réseaux corporels en environnement intérieur dans le contexte de l’analyse du mouvement et de la navigation de groupe. Dans ce travail, une approche de simulation pour le cala de propagation est présenté. Le simulateur de canal de propagation est basé sur les techniques de tracé de rayons et l’approche de simulation est basée sur l’utilisation d’antennes perturbées et l’utilisation des données de capture de mouvement pour la modélisation de la mobilité humaine. Premièrement, nous étudions la question de l’antenne et l’influence de la proximité du corps humain sur diagramme de rayonnement de l’antenne. En outre, un modèle simple utilisé pour prédire le diagramme de rayonnement d’une antenne placée à proximité d’un corps humain. Deuxièmement, le simulateur physique est présenté et l’approche de simulation est introduite. Afin de vérifier l’approche proposée, des simulations préliminaires ont été effectuées et une première comparaison avec des donnes de mesures disponibles est faite. Enfin, une campagne de mesure spécifique joignant les données radio et les données de capture de mouvement a été exploitée pour valider et évaluer les résultats de la simulation. / Recent advances in wireless technologies and system, empowered by the miniaturization of devices, give rise to a new generation of Personal Area Networks allowing communications around the human body : Body Area networks. This thesis studies the Body Area Network channels in indoor environment in the context of motion analysis and group navigation. In this work a simulation approach for BAN channels is presented. The propagation channel simulator is based on ray tracing and the simulation approach is based on using perturbed antennas and the use of motion capture data for modelling the human mobility. Firstly, we investigate the antenna issue and the influence of the human body prox- imity on antenna radiation pattern. Besides, a simple model used to predict the antenna radiation pattern placed in proximity to a human body. Secondly, the physical sim- ulator is presented and the simulation approach is introduced. In order to check the proposed approach, preliminary simulations were carried out and a first comparison with available measurement data is made. Finally, a specific measurement campaign jointing radio data and motion capture data was exploited to validate and evaluate the simulation results.