A study of non-exercise activity thermogenesis using lego sensors

According to the world health organization (WHO), there are one billion adults globally who are overweight and 300 million of them are obese. This increase in obesity has led to increased popularity of activity monitors such as pedometers, which help to estimate energy expenditure and other paramete...

Full description

Saved in:
Bibliographic Details
Main Author: Koh, Veronica Wei Ping.
Other Authors: Chen I-Ming
Format: Final Year Project
Language:English
Published: 2010
Subjects:
Online Access:http://hdl.handle.net/10356/39759
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-39759
record_format dspace
spelling sg-ntu-dr.10356-397592023-03-04T18:20:03Z A study of non-exercise activity thermogenesis using lego sensors Koh, Veronica Wei Ping. Chen I-Ming School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering::Bio-mechatronics According to the world health organization (WHO), there are one billion adults globally who are overweight and 300 million of them are obese. This increase in obesity has led to increased popularity of activity monitors such as pedometers, which help to estimate energy expenditure and other parameters such as the number of steps taken daily. In this study, the focus is on non-exercise activity thermogenesis (NEAT), which is an important component of an individual’s total daily energy expenditure (TDEE). In particular, three non-exercise activities investigated include sitting, standing and walking at different speeds and the parameters of interest are steps taken, time allocation for different activities and energy expenditure. A Lego NXT controller and a third party acceleration sensor were used to investigate NEAT due to their ease of usage and availability of programming resources. RobotC and MATLAB programming algorithms were developed for data acquisition and analysis of signals. Various studies were conducted in this project with the acceleration sensor either mounted on the thigh or the hip to capture human motion. The different studies were conducted with the aim of estimating parameters optimally with acceptable accuracy, as well as sufficiently long period of activity monitoring. The effect of sampling frequency on steps estimation accuracy was studied to obtain an optimal frequency of about 5Hz, where percentage error for steps estimation was minimal. Filtering algorithm of the acceleration signals was also developed using MATLAB to reduce noise and increase steps estimation accuracy. In order to overcome data space limitation of the NXT controller, adaptive sampling for sensor readings was also developed to increase sampling frequency during more dynamic motion like walking and reduce it during relatively static motion like sitting and standing. Bachelor of Engineering (Mechanical Engineering) 2010-06-04T01:05:04Z 2010-06-04T01:05:04Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/39759 en Nanyang Technological University 90 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
spellingShingle DRNTU::Engineering::Mechanical engineering::Bio-mechatronics
Koh, Veronica Wei Ping.
A study of non-exercise activity thermogenesis using lego sensors
description According to the world health organization (WHO), there are one billion adults globally who are overweight and 300 million of them are obese. This increase in obesity has led to increased popularity of activity monitors such as pedometers, which help to estimate energy expenditure and other parameters such as the number of steps taken daily. In this study, the focus is on non-exercise activity thermogenesis (NEAT), which is an important component of an individual’s total daily energy expenditure (TDEE). In particular, three non-exercise activities investigated include sitting, standing and walking at different speeds and the parameters of interest are steps taken, time allocation for different activities and energy expenditure. A Lego NXT controller and a third party acceleration sensor were used to investigate NEAT due to their ease of usage and availability of programming resources. RobotC and MATLAB programming algorithms were developed for data acquisition and analysis of signals. Various studies were conducted in this project with the acceleration sensor either mounted on the thigh or the hip to capture human motion. The different studies were conducted with the aim of estimating parameters optimally with acceptable accuracy, as well as sufficiently long period of activity monitoring. The effect of sampling frequency on steps estimation accuracy was studied to obtain an optimal frequency of about 5Hz, where percentage error for steps estimation was minimal. Filtering algorithm of the acceleration signals was also developed using MATLAB to reduce noise and increase steps estimation accuracy. In order to overcome data space limitation of the NXT controller, adaptive sampling for sensor readings was also developed to increase sampling frequency during more dynamic motion like walking and reduce it during relatively static motion like sitting and standing.
author2 Chen I-Ming
author_facet Chen I-Ming
Koh, Veronica Wei Ping.
format Final Year Project
author Koh, Veronica Wei Ping.
author_sort Koh, Veronica Wei Ping.
title A study of non-exercise activity thermogenesis using lego sensors
title_short A study of non-exercise activity thermogenesis using lego sensors
title_full A study of non-exercise activity thermogenesis using lego sensors
title_fullStr A study of non-exercise activity thermogenesis using lego sensors
title_full_unstemmed A study of non-exercise activity thermogenesis using lego sensors
title_sort study of non-exercise activity thermogenesis using lego sensors
publishDate 2010
url http://hdl.handle.net/10356/39759
_version_ 1759856333985677312