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.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/104868 |
| Date | 30 August 2021 |
| Creators | Goncalves, Rafael dos Santos |
| Contributors | Mechanical Engineering, Sarlo, Rodrigo, Barry, Oumar, West, Robert L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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