PMID- 36726417
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230203
IS  - 2228-7477 (Print)
IS  - 2228-7477 (Electronic)
IS  - 2228-7477 (Linking)
VI  - 12
IP  - 4
DP  - 2022 Oct-Dec
TI  - An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness 
      Level Based on Electroencephalography Signal and Vehicle Dynamics Data.
PG  - 294-305
LID - 10.4103/jmss.jmss_124_21 [doi]
AB  - BACKGROUND: Drowsy driving is one of the leading causes of severe accidents 
      worldwide. In this study, an analyzing method based on drowsiness level proposed 
      to detect drowsiness through electroencephalography (EEG) measurements and 
      vehicle dynamics data. METHODS: A driving simulator was used to collect brain 
      data in the alert and drowsy states. The tests were conducted on 19 healthy men. 
      Brain signals from the parietal, occipital, and central parts were recorded. 
      Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages 
      assessment. This study used an innovative method, analyzing drowsiness EEG data 
      were in respect to ORD instead of time. Thirteen features of EEG signal were 
      extracted, then through Neighborhood Component Analysis, a feature selection 
      method, 5 features including mean, standard deviation, kurtosis, energy, and 
      entropy are selected. Six classification methods including K-nearest neighbors 
      (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines 
      Regression, and Ensemble Regression are employed. Besides, the lateral position 
      and steering angle as a vehicle dynamic data were used to detect drowsiness, and 
      the results were compared with classification result based on EEG data. RESULTS: 
      According to the results of classifying EEG data, classification tree and 
      ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at 
      the moderate level, respectively. Furthermore, the classification results 
      demonstrate that if only the single-channel P4 is used, higher performance can 
      achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). 
      Classification tree classifier and regression classifiers showed 91.31% and 
      91.12% performance with data from single-channel P4. The best classification 
      results based on vehicle dynamic data were 75.11 through KNN classifier. 
      CONCLUSION: According to this study, driver drowsiness could be detected at the 
      moderate drowsiness level based on features extracted from a single-channel P4 
      data.
CI  - Copyright: (c) 2022 Journal of Medical Signals & Sensors.
FAU - Houshmand, Sara
AU  - Houshmand S
AD  - Department of Mechanical Engineering, KN. Toosi University of Technology, Tehran, 
      Iran.
FAU - Kazemi, Reza
AU  - Kazemi R
AD  - Department of Mechanical Engineering, KN. Toosi University of Technology, Tehran, 
      Iran.
FAU - Salmanzadeh, Hamed
AU  - Salmanzadeh H
AD  - Department of Industrial Engineering, KN. Toosi University of Technology, Tehran, 
      Iran.
LA  - eng
PT  - Journal Article
DEP - 20221110
PL  - India
TA  - J Med Signals Sens
JT  - Journal of medical signals and sensors
JID - 101577416
PMC - PMC9885505
OTO - NOTNLM
OT  - Driving simulator
OT  - drowsy driving
OT  - electroencephalography signal
OT  - feature extraction
OT  - signal classification
OT  - supervised learning methods
OT  - vehicle dynamics
COIS- There are no conflicts of interest.
EDAT- 2023/02/03 06:00
MHDA- 2023/02/03 06:01
PMCR- 2022/11/10
CRDT- 2023/02/02 01:55
PHST- 2021/05/23 00:00 [received]
PHST- 2021/08/25 00:00 [revised]
PHST- 2021/10/28 00:00 [accepted]
PHST- 2023/02/02 01:55 [entrez]
PHST- 2023/02/03 06:00 [pubmed]
PHST- 2023/02/03 06:01 [medline]
PHST- 2022/11/10 00:00 [pmc-release]
AID - JMSS-12-294 [pii]
AID - 10.4103/jmss.jmss_124_21 [doi]
PST - epublish
SO  - J Med Signals Sens. 2022 Nov 10;12(4):294-305. doi: 10.4103/jmss.jmss_124_21. 
      eCollection 2022 Oct-Dec.