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Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)

Doctor of Philosophy in Mechatronic Engineering

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Bibliographic Details
Main Author: Chawki, Berkai
Other Authors: Hariharan, Muthusamy, Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2017
Subjects:
Body mass index (BMI)
Obesity
Speech recognition
BMI prediction
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77987
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spelling my.unimap-779872023-03-06T02:54:04Z Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI) Chawki, Berkai Hariharan, Muthusamy, Dr. Body mass index (BMI) Obesity Speech recognition BMI prediction Doctor of Philosophy in Mechatronic Engineering Obesity and overweight have been a growing concern due to their negative impacts on human‘s health. Obesity is considered as a major cause of some serious diseases such as diabetes, cardiovascular diseases, and metabolic syndrome, and it has become epidemic. Today, body mass index (BMI) is widely used as a tool to classify normal weight, overweight, underweight and obesity. These measurements are sometimes not suitable for remote healthcare or u-healthcare supporting general treatment and emergency medical service in real time at remote locations. The researchers have explored the association between speech recognition and BMI. Speech signals have a close relation with BMI status, which is predicted by a combination of key features. The purpose of this research work is to predict BMI status (normal, overweight and obese) using speech signal without weight and height measurements. In this research work, wavelet packet based nonlinear entropy features and feature selection algorithms were proposed to predict BMI status via speech signal of normal, obese and overweight subjects. The recorded speech signal (/ah/ sounds) were decomposed up to level five using wavelet packet transform (WPT). Several features were extracted from the wavelet packet coefficients and an Analysis of Variance (ANOVA) test. 2017 2023-03-06T02:49:01Z 2023-03-06T02:49:01Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77987 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Body mass index (BMI)
Obesity
Speech recognition
BMI prediction
spellingShingle Body mass index (BMI)
Obesity
Speech recognition
BMI prediction
Chawki, Berkai
Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
description Doctor of Philosophy in Mechatronic Engineering
author2 Hariharan, Muthusamy, Dr.
author_facet Hariharan, Muthusamy, Dr.
Chawki, Berkai
format Thesis
author Chawki, Berkai
author_sort Chawki, Berkai
title Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
title_short Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
title_full Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
title_fullStr Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
title_full_unstemmed Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
title_sort non-linear features and feature selection algorithms for speech based prediction of body mass index (bmi)
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2017
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77987
_version_ 1772813067757813760
score 13.222552

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