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

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 us...

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Format:	Thesis
Language:	English
Subjects:	Body mass index (BMI) Obesity Speech recognition BMI prediction
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/4/Chawki%20Berkai.pdf
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id	my-unimap-77987
record_format	uketd_dc
spelling	my-unimap-779872023-03-06T02:54:04Z Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI) Hariharan, Muthusamy, Dr. 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. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77987 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/1/Page%201-24.pdf 5b12aeafd9be5a60de633dc6c54fd486 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/2/Full%20text.pdf eb2e109400cf276ab2cf6de82c40acd9 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/4/Chawki%20Berkai.pdf 4f1090a21fcf42eb6b4adb40befde9b6 Universiti Malaysia Perlis (UniMAP) Body mass index (BMI) Obesity Speech recognition BMI prediction School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Hariharan, Muthusamy, Dr.
topic	Body mass index (BMI) Obesity Speech recognition BMI prediction
spellingShingle	Body mass index (BMI) Obesity Speech recognition BMI prediction Non-linear features and feature selection algorithms for speech based prediction of body mass index (BMI)
description	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.
format	Thesis
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)
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77987/4/Chawki%20Berkai.pdf
_version_	1776104240211558400

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

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