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

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MAHMOODI MOHAMMAD; NAHVI ALI

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Journal Paper

Paper Information

Journal: JOURNAL OF SLEEP SCIENCES Year:2018 | Volume:3 | Issue:3-4 Page(s): 53-62

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Information Journal Paper

Title

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

Author(s)

MAHMOODI MOHAMMAD | NAHVI ALI | Issue Writer Certificate

Keywords

Sleep deprivation

Automobile driving

Surface electromyography

Abstract

Background and Objective: Sleep deprivation is an important cause of driver drowsiness. Surface electromyogra-phy (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% preci-sion, 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 sig-nificant change in the muscle contraction level.

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APA: Copy

MAHMOODI, MOHAMMAD, & NAHVI, ALI. (2018). Investigation of Sleep Deprivation Effect on Driver’ s Electromyography Signal Features in a Driving Simulator. JOURNAL OF SLEEP SCIENCES, 3(3-4), 53-62. SID. https://sid.ir/paper/354816/en

Vancouver: Copy

MAHMOODI MOHAMMAD, NAHVI ALI. Investigation of Sleep Deprivation Effect on Driver’ s Electromyography Signal Features in a Driving Simulator. JOURNAL OF SLEEP SCIENCES[Internet]. 2018;3(3-4):53-62. Available from: https://sid.ir/paper/354816/en

IEEE: Copy

MOHAMMAD MAHMOODI, and ALI NAHVI, “Investigation of Sleep Deprivation Effect on Driver’ s Electromyography Signal Features in a Driving Simulator,” JOURNAL OF SLEEP SCIENCES, vol. 3, no. 3-4, pp. 53–62, 2018, [Online]. Available: https://sid.ir/paper/354816/en

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