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The study of time domain features of EMG signals for detecting driver’s drowsiness

Fatigue or drowsiness is one of the major causes of traffic accidents in Malaysia. Physiological signals such as EMG is a useful input to detect drowsiness in drivers. The time domain features are easy to compute and well researched in the field of EMG hand motion detection. The focus of this paper...

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Main Authors: Faradila, Naim, Mahfuzah, Mustafa, Norizam, Sulaiman, Noor Aisyah, Ab Rahman
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/42244/1/The%20study%20of%20time%20domain%20features%20of%20EMG%20signals.pdf
http://umpir.ump.edu.my/id/eprint/42244/2/The%20study%20of%20time%20domain%20features%20of%20emg%20signals%20for%20detecting%20driver%E2%80%99s%20drowsiness_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42244/
https://doi.org/10.1007/978-981-33-4597-3_39
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spelling my.ump.umpir.422442024-10-30T04:24:54Z http://umpir.ump.edu.my/id/eprint/42244/ The study of time domain features of EMG signals for detecting driver’s drowsiness Faradila, Naim Mahfuzah, Mustafa Norizam, Sulaiman Noor Aisyah, Ab Rahman T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Fatigue or drowsiness is one of the major causes of traffic accidents in Malaysia. Physiological signals such as EMG is a useful input to detect drowsiness in drivers. The time domain features are easy to compute and well researched in the field of EMG hand motion detection. The focus of this paper is to find the best set of time domain features to detect drowsiness in drivers’ EMG signal from biceps brachii muscle. This study analyzes the time domain features of EMG signals in detecting the drowsiness in drivers during a 2 h simulated driving session. Nine time-domain features are applied to all 15 samples and classified using six classifiers. The best single feature for the long duration signal is the mean absolute value slope (MAVS) with 80% accuracy using Naïve Bayes (NB) classifiers. All features combined gives the highest accuracy of 85% using linear discriminant analysis (LDA) classifier. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42244/1/The%20study%20of%20time%20domain%20features%20of%20EMG%20signals.pdf pdf en http://umpir.ump.edu.my/id/eprint/42244/2/The%20study%20of%20time%20domain%20features%20of%20emg%20signals%20for%20detecting%20driver%E2%80%99s%20drowsiness_ABS.pdf Faradila, Naim and Mahfuzah, Mustafa and Norizam, Sulaiman and Noor Aisyah, Ab Rahman (2022) The study of time domain features of EMG signals for detecting driver’s drowsiness. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang. pp. 427-438., 730. ISSN 1876-1100 ISBN 978-981334596-6 (Published) https://doi.org/10.1007/978-981-33-4597-3_39
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Faradila, Naim
Mahfuzah, Mustafa
Norizam, Sulaiman
Noor Aisyah, Ab Rahman
The study of time domain features of EMG signals for detecting driver’s drowsiness
description Fatigue or drowsiness is one of the major causes of traffic accidents in Malaysia. Physiological signals such as EMG is a useful input to detect drowsiness in drivers. The time domain features are easy to compute and well researched in the field of EMG hand motion detection. The focus of this paper is to find the best set of time domain features to detect drowsiness in drivers’ EMG signal from biceps brachii muscle. This study analyzes the time domain features of EMG signals in detecting the drowsiness in drivers during a 2 h simulated driving session. Nine time-domain features are applied to all 15 samples and classified using six classifiers. The best single feature for the long duration signal is the mean absolute value slope (MAVS) with 80% accuracy using Naïve Bayes (NB) classifiers. All features combined gives the highest accuracy of 85% using linear discriminant analysis (LDA) classifier.
format Conference or Workshop Item
author Faradila, Naim
Mahfuzah, Mustafa
Norizam, Sulaiman
Noor Aisyah, Ab Rahman
author_facet Faradila, Naim
Mahfuzah, Mustafa
Norizam, Sulaiman
Noor Aisyah, Ab Rahman
author_sort Faradila, Naim
title The study of time domain features of EMG signals for detecting driver’s drowsiness
title_short The study of time domain features of EMG signals for detecting driver’s drowsiness
title_full The study of time domain features of EMG signals for detecting driver’s drowsiness
title_fullStr The study of time domain features of EMG signals for detecting driver’s drowsiness
title_full_unstemmed The study of time domain features of EMG signals for detecting driver’s drowsiness
title_sort study of time domain features of emg signals for detecting driver’s drowsiness
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/42244/1/The%20study%20of%20time%20domain%20features%20of%20EMG%20signals.pdf
http://umpir.ump.edu.my/id/eprint/42244/2/The%20study%20of%20time%20domain%20features%20of%20emg%20signals%20for%20detecting%20driver%E2%80%99s%20drowsiness_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42244/
https://doi.org/10.1007/978-981-33-4597-3_39
_version_ 1822924719665446912
score 13.232389

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