This work demonstrates an application of Vibration-Based Occupant Inference (VBOI) in frailty analysis. The rise of both Internet-of-Things (IoT) and VBOI provide new techniques to perform gait analysis via footstep-induced vibration which can be analyzed for early detection of human frailty. Thus, this work provides an application of VBOI to passively track gait parameters (e.g., gait speed) using floor-mounted accelerometers as opposed to using a manual chronometer as it is commonly performed by healthcare professionals. The first part of this thesis describes the techniques used for footstep detection by measuring the power of the footstep-generated vibration waves. The extraction of temporal gait parameters from consecutive footsteps can then be used to estimate temporal features such as cadence and stride time variation. VBOI provides many algorithms to accurately detect when a human-induced vibration event happened, however, spatial information is also needed for many gait parameters used in frailty diagnosis. Detecting where an event happened is a complicated problem because footsteps waves travel and decay in different ways according to the medium (floor system), the number of people walking, and even the walking speed. Therefore, the second part of this work will utilize an energy-based approach of footstep localization in which it is assumed that footstep waves decay exponentially as they travel across the medium. The results from this approach are then used to calculate spatial and tempo-spatial parameters. The main goal of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection in a healthcare setting. / Master of Science / Human frailty is responsible for one of the highest healthcare costs and the death of many people every year. Although anyone suffering from frailty has a higher chance of death, it is particularly dangerous for the elderly population and for those suffering from other comorbidities. Diagnosing frailty is hard because it usually happens slowly over time. However, it has been shown that changes in some walking parameters (such as gait speed) can be an early indication of frailty. Many technologies have been created in order to track gait parameters, many of which either require expensive equipment (e.g., force plates) or the use of wearable devices, which can introduce privacy concerns. It has been proposed in the literature that Vibration-Based Occupant Inference (VBOI) techniques could be used in healthcare applications. Such algorithms measure footstep-induced vibration waves in order to detect and track footsteps. This system can provide several advantages in frailty analysis because of its affordability, ease of use, and little impact on patients' privacy. Therefore, the aim of this study is to understand the applicability of VBOI algorithms in gait analysis for frailty detection to be used in a healthcare setting. This thesis will proceed as follows: 1- The demonstration of an energy-based footstep detection and localization algorithm in VBOI. 2 - The application of such algorithms for gait parameters extraction with simulated frail walkers. 3 - Finally, an analysis of the proposed VBOI techniques for deployment in a real hospital setting.

This work addresses the problem of localizing an impact in a dispersive medium (waveguide) using a network of vibration sensors (accelerometers), distributed at various locations in the waveguide, measuring (and detecting the arrival of) the impact-generated seismic wave. In particular, the last part of this document focuses on the problem of localizing footsteps using underfloor accelerometers. The author believes the outcomes of this work pave the way for realizing real-time indoor occupant tracking using underfloor accelerometers; a system that is tamper-proof and non-intrusive compared to occupant tracking systems that rely on video image processing. A dispersive waveguide (e.g., a floor) causes the impact-generated wave to distort with the traveled distance and renders conventional time of flight localization methods inaccurate. Therefore, this work focuses on laying the foundation of a new alternative approach to impact localization in dispersive waveguides. In this document, localization algorithms, including wave-signal detection and signal processing, are developed utilizing the fact that the generated wave's energy is attenuated with the traveled distance. The proposed localization algorithms were evaluated using simulations and experiments of hammer impacts, in addition to occupant tracking experiments. The experiments were carried out on an instrumented floor section inside a smart building. As will be explained in this document, energy-based localization will turn out to be computationally cheap and more accurate than conventional time of flight techniques. / PHD / When a person walks, each footstep impact generates a tiny floor-quake. The floor-quake sends a shock wave traveling along the floor, and causes the floor to vibrate. If these vibrations are sensed/measured at different locations in the floor, then the measurements can be used to estimate the individual footstep impact locations. Estimating the location of each footstep impact can then be utilized to track the walking path of the person. This dissertation proposes a novel footstep location estimation approach. The localization approach uses a group of underfloor vibration sensors, called accelerometers, to measure the footstep-generated floor vibration. Then, the sensor measurements are used to estimate footstep locations. Footstep location estimates are generated using the fact that the strength/energy of the generated wave is absorbed by the floor, and consequently the wave energy is attenuated with the traveled distance. The proposed footstep localization approach can be used to track occupants inside buildings, providing a tracking system that is non-intrusive compared to tracking occupants using a system of cameras and a video image-processing software.