Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator

Background and Objective: Sleep deprivation is an important cause of driver drowsiness. Surface electromyography (sEMG) of the upper arm and the shoulder is an important physiological signal affected by the driver drowsiness. The objective of this paper is to derive the pattern of sleep-deprived dr...

Full description

Bibliographic Details
Main Authors: Mohammad Mahmoodi, Ali Nahvi
Format: Article
Language:English
Published: Tehran University of Medical Sciences 2019-06-01
Series:Journal of Sleep Sciences
Subjects:
Online Access:https://jss.tums.ac.ir/index.php/jss/article/view/126
id doaj-33fc1962cf2b48c5bb8fa335e3e9603d
record_format Article
spelling doaj-33fc1962cf2b48c5bb8fa335e3e9603d2020-12-02T05:38:57ZengTehran University of Medical SciencesJournal of Sleep Sciences2476-29382476-29462019-06-0133-4Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving SimulatorMohammad Mahmoodi0Ali Nahvi1Department of Mechanical Engineering, School of Mechanical Engineering, Khajeh Nasir Toosi University of Technology, Tehran, IranDepartment of Mechanical Engineering, School of Mechanical Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran Background and Objective: Sleep deprivation is an important cause of driver drowsiness. Surface electromyography (sEMG) of the upper arm and the shoulder is an important physiological signal affected by the driver drowsiness. The objective of this paper is to derive the pattern of sleep-deprived drivers’ sEMG. Materials and Methods: The tests were conducted on 7 men with no sleep disorder aged between 25 and 50 years in a driving simulator. Each subject participated in the tests once without sleep deprivation and another time with two hours of sleep in the 24-hour period before the tests. The sEMG signal from the upper arm and shoulder muscles were measured for the mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Four features including power spectral kurtosis (SK), mean frequency, absolute amplitude, and root mean square (RMS) were extracted. Results: The k-nearest neighbors (k-NN) algorithm classifier detected drowsiness with 90% accuracy, 82% precision, 77% sensitivity, and 94% specificity. Driver’s sleep deprivation can be detected through sEMG signal with 85% accuracy, 80% precision, 70% sensitivity, and 88% specificity. Conclusion: The sEMG signal amplitude and the frequency content of the sleep-deprived subjects were higher than those of the normal subjects by 37% and 15%, respectively. For the sleep-deprived subjects, muscle contraction did not change much in transition between the last two levels of drowsiness, while the normal subjects experienced 27% drop in this transition. At the last level of drowsiness, the sleep-deprived subjects experienced mental drowsiness without significant change in the muscle contraction level. https://jss.tums.ac.ir/index.php/jss/article/view/126Sleep deprivationAutomobile drivingSurface electromyography
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Mahmoodi
Ali Nahvi
spellingShingle Mohammad Mahmoodi
Ali Nahvi
Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
Journal of Sleep Sciences
Sleep deprivation
Automobile driving
Surface electromyography
author_facet Mohammad Mahmoodi
Ali Nahvi
author_sort Mohammad Mahmoodi
title Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
title_short Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
title_full Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
title_fullStr Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
title_full_unstemmed Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator
title_sort investigation of sleep deprivation effect on driver’s electromyography signal features in a driving simulator
publisher Tehran University of Medical Sciences
series Journal of Sleep Sciences
issn 2476-2938
2476-2946
publishDate 2019-06-01
description Background and Objective: Sleep deprivation is an important cause of driver drowsiness. Surface electromyography (sEMG) of the upper arm and the shoulder is an important physiological signal affected by the driver drowsiness. The objective of this paper is to derive the pattern of sleep-deprived drivers’ sEMG. Materials and Methods: The tests were conducted on 7 men with no sleep disorder aged between 25 and 50 years in a driving simulator. Each subject participated in the tests once without sleep deprivation and another time with two hours of sleep in the 24-hour period before the tests. The sEMG signal from the upper arm and shoulder muscles were measured for the mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Four features including power spectral kurtosis (SK), mean frequency, absolute amplitude, and root mean square (RMS) were extracted. Results: The k-nearest neighbors (k-NN) algorithm classifier detected drowsiness with 90% accuracy, 82% precision, 77% sensitivity, and 94% specificity. Driver’s sleep deprivation can be detected through sEMG signal with 85% accuracy, 80% precision, 70% sensitivity, and 88% specificity. Conclusion: The sEMG signal amplitude and the frequency content of the sleep-deprived subjects were higher than those of the normal subjects by 37% and 15%, respectively. For the sleep-deprived subjects, muscle contraction did not change much in transition between the last two levels of drowsiness, while the normal subjects experienced 27% drop in this transition. At the last level of drowsiness, the sleep-deprived subjects experienced mental drowsiness without significant change in the muscle contraction level.
topic Sleep deprivation
Automobile driving
Surface electromyography
url https://jss.tums.ac.ir/index.php/jss/article/view/126
work_keys_str_mv AT mohammadmahmoodi investigationofsleepdeprivationeffectondriverselectromyographysignalfeaturesinadrivingsimulator
AT alinahvi investigationofsleepdeprivationeffectondriverselectromyographysignalfeaturesinadrivingsimulator
_version_ 1724409109824929792