ORCID Identifier(s)

0000-0002-1514-5830

Graduation Semester and Year

2020

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Manfred Huber

Abstract

This thesis is aimed at a substantial health problem among the elderly population that is “Fall”, a major cause of accidental home deaths. Studies show approximately one-third of community-dwelling people over 65 years of age will experience one or more falls each year. The balance and walking pattern are useful to determine the risk of fall in an individual and is highly influenced by several parameters and conditions. The deterioration in the balance and walking stability of an individual can occur because of the natural processes related to aging or as a result of various underlying health conditions, fatigue, muscle tone, or impaired balance. The Tinetti-test is widely used to assess the gait and balance in elder adults to determine the perception of balance and stability during daily activities and fear of falling. It is considered a good indicator of the fall risk of an individual. In this research, we aimed to provide a new way for non-intrusive balance assessment and Tinetti score prediction by creating a Machine Learning model for predicting fall risk and early detection of the onset of chronic health conditions. This will help to improve eldercare by facilitating constant monitoring and by reducing the white-coat syndrome that inhibits clinical examinations. This thesis mainly focuses on designing algorithms to extract the balance parameters for the quiet standing instances capable of differentiating normal or abnormal patterns for an individual from the pressure readings obtained from a smart floor. A variety of time and frequency domain features are build based on the center of pressure (COP) values. These COP values were obtained from time-series data from a pressure monitoring smart floor. A classification model is build using a support vector machine for distinguishing 30 individuals based solely on these balance parameters. Further, using these parameters, a regression model is built to predict the balance component as well as the compete Tinetti score of an individual which is used to predict the fall risk. This is a novel approach for Tinetti score prediction using balance analysis.

Keywords

TINETTI, Fall risk, Fall, Smartfloor, Machine learning, AI, Balance, Balance parameters

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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