Sparsity based localization in medical applications: Medical implant in-body localization using wireless body sensor networks, and indoor tracking of patients for assistive healthcare

Wireless assistive healthcare technologies, wireless communication medical devices, and body sensor networks for medical diagnostics and therapeutics have attracted increasing attention recently. Modern Healthcare systems use wearable and implantable medical devices to capture and transmit the medical information and vital signs of the patients and control the organs activities.;In this dissertation, we explore the applications of wireless sensor network in biomedical systems including in-body implant localization and indoor tracking of patients for assistive healthcare. The general problem of localization in wireless sensor networks is also considered by modifying the existing methods and designing innovative techniques to reduce the computational complexity and data transmission in sensor networks, as well as designing and implementing new localization methods based on spatial sparsity.;Some unique challenges exist for in-body localization of a medical implant due to the complex nature within the human body, such as the dependency of the propagation velocity on type of the tissues, the dependency of the path loss model on tissue thickness, and the multipath problem caused by signal reflections at organ boundaries. Furthermore, the safety restrictions on signal power and bandwidth with regard to the protection of human health also make it more difficult to achieve accurate implant location estimation.;In this dissertation, we develop novel and effective methods for location estimation in medical applications including medical implant in-body localization using wireless body sensor networks, and also indoor localization, tracking and fall detection of patients for assistive healthcare. The proposed method estimates the location directly without going through the intermediate stage of Received Signal Strength (RSS) or Time of Arrival (TOA) estimations. The simulation results demonstrate that the proposed methods are very accurate in location estimation even using a small number of sensors, and a small number of signal samples. Furthermore, the system shows robust operations and high performance in noisy environments (low SNRs). It means that we are able to achieve high localization accuracy even with very low wireless transmitted power which helps to reduce the size of the implantable or wearable device, increase the device's battery life, and also reduce the risk of interfering with other users of the same band.