Classification of drivers’ workload through electrocardiography

With the development of technology, many kinds of attractive functions such as answering phone call, playing games, and listening to music have been added to car system in order to make driving more convenient and interesting. Meanwhile, the number of car accidents is increasing due to these extra d...

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Main Author: Pang, Bo
Other Authors: Huang Guangbin
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/68307
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-683072023-07-07T17:19:40Z Classification of drivers’ workload through electrocardiography Pang, Bo Huang Guangbin School of Electrical and Electronic Engineering DRNTU::Engineering With the development of technology, many kinds of attractive functions such as answering phone call, playing games, and listening to music have been added to car system in order to make driving more convenient and interesting. Meanwhile, the number of car accidents is increasing due to these extra distracting functions. It is obvious that these extra distracting functions can affect human workload of drivers. Therefore, studying human workload of drivers using these functions is very important to safety driving. This final year project aims to classify human workload during driving by two methods and to recommend one of them as better method. The two methods are classification by ECG and by SWAT. This project is divided into two parts: the first part is to collect the required data by conducting an experiment by a partly automatic driving system; the second part is to analyze the data, classify the human workload, and test the accuracy of classification by ELM to find the better method. Finally, the analysis showed more than 97% accuracy of detecting levels of workload by ECG classification. This means that our project is successful and ECG data is a better way to classify human workload. Bachelor of Engineering 2016-05-25T05:52:47Z 2016-05-25T05:52:47Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68307 en Nanyang Technological University 58 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
spellingShingle DRNTU::Engineering
Pang, Bo
Classification of drivers’ workload through electrocardiography
description With the development of technology, many kinds of attractive functions such as answering phone call, playing games, and listening to music have been added to car system in order to make driving more convenient and interesting. Meanwhile, the number of car accidents is increasing due to these extra distracting functions. It is obvious that these extra distracting functions can affect human workload of drivers. Therefore, studying human workload of drivers using these functions is very important to safety driving. This final year project aims to classify human workload during driving by two methods and to recommend one of them as better method. The two methods are classification by ECG and by SWAT. This project is divided into two parts: the first part is to collect the required data by conducting an experiment by a partly automatic driving system; the second part is to analyze the data, classify the human workload, and test the accuracy of classification by ELM to find the better method. Finally, the analysis showed more than 97% accuracy of detecting levels of workload by ECG classification. This means that our project is successful and ECG data is a better way to classify human workload.
author2 Huang Guangbin
author_facet Huang Guangbin
Pang, Bo
format Final Year Project
author Pang, Bo
author_sort Pang, Bo
title Classification of drivers’ workload through electrocardiography
title_short Classification of drivers’ workload through electrocardiography
title_full Classification of drivers’ workload through electrocardiography
title_fullStr Classification of drivers’ workload through electrocardiography
title_full_unstemmed Classification of drivers’ workload through electrocardiography
title_sort classification of drivers’ workload through electrocardiography
publishDate 2016
url http://hdl.handle.net/10356/68307
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