The electrooculography (EOG) signal is considered most suitable for drowsiness detection. Besides its simplicity and low cost, EOG signals are not affected by environmental factors such as light intensity and driver movement. However, existing EOG-based drowsiness detection techniques employ arbitrarily chosen features for classifier training, leading to results that are less robust against changes in the measurement method, noise level, and individual subject variability. In this study, we propose a system dynamics-based approach to drowsiness detection. The EOG signal is treated as a neurophysiological response of the oculomotor system. Each blink action is considered as a result of a series of neuron firing impulses entering the system. Blink signatures are thus extracted to identify the system transfer function, from which system poles are computed to characterize the drowsiness state of the subject. It was found that the location of system poles on the pole–zero map for blink signatures from an alert state was distinctly different from those from a drowsy state. A simple criterion was subsequently developed for drowsiness detection by counting the ratio of real and complex poles of the system over any given period of time. The proposed methodology is a systematic approach and does not require extensive classifier training. It is robust against variations in the subject condition, sensor placement, noise level, and blink rate.

References

References
1.
National Sleep Foundation,
2016
, “
Facts and Stats
,” National Sleep Foundation, Washington, DC, accessed Mar. 1, 2016, http://drowsydriving.org/about/facts-and-stats/
2.
Horne
,
J.
, and
Reyner
,
L.
,
1999
, “
Vehicle Accidents Related to Sleep: A Review
,”
Occup. Environ. Med.
,
56
(
5
), pp.
289
294
.
3.
Dong
,
Y.
,
Hu
,
Z.
,
Uchimura
,
K.
, and
Murayama
,
N.
,
2011
, “
Driver Inattention Monitoring System for Intelligent Vehicles: A Review
,”
IEEE Trans. Intell. Transp. Syst.
,
12
(
2
), pp.
596
614
.
4.
Sahayadhas
,
A.
,
Sundaraj
,
K.
, and
Murugappan
,
M.
,
2012
, “
Detecting Driver Drowsiness Based on Sensors: A Review
,”
Sensors
,
12
(
12
), pp.
16937
16953
.
5.
Liu
,
C. C.
,
Hosking
,
S. G.
, and
Lenné
,
M. G.
,
2009
, “
Predicting Driver Drowsiness Using Vehicle Measures: Recent Insights and Future Challenges
,”
J. Saf. Res.
,
40
(
4
), pp.
239
245
.
6.
Ji
,
Q.
, and
Yang
,
X.
,
2002
, “
Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance
,”
Real-Time Imaging
,
8
(
5
), pp.
357
377
.
7.
Johns
,
M. W.
,
Tucker
,
A.
,
Chapman
,
R.
,
Crowley
,
K.
, and
Michael
,
N.
,
2007
, “
Monitoring Eye and Eyelid Movements by Infrared Reflectance Oculography to Measure Drowsiness in Drivers
,”
Somnologie-Schlafforsch. Schlafmedizin
,
11
(
4
), pp.
234
242
.
8.
Wahlstrom
,
E.
,
Masoud
,
O.
, and
Papanikolopoulos
,
N.
,
2003
, “
Vision-Based Methods for Driver Monitoring
,”
IEEE Intelligent Transportation Systems
(
ITSC
), Shanghai, China, Oct. 12–15, pp.
903
908
.
9.
Liu
,
D.
,
Sun
,
P.
,
Xiao
,
Y.
, and
Yin
,
Y.
,
2010
, “
Drowsiness Detection Based on Eyelid Movement
,”
2nd International Workshop on Education Technology and Computer Science
(
ETCS
), Wuhan, China, Mar. 6–7, pp.
49
52
.
10.
Bhandari
,
G. M.
,
Durge
,
A.
,
Bidwai
,
A.
, and
Aware
,
U.
,
2014
, “
Yawning Analysis for Driver Drowsiness Detection
,”
Int. J. Eng. Res. Technol.
,
3
(
2
), pp.
502
505
.
11.
Mittal
,
A.
,
Kumar
,
K.
,
Dhamija
,
S.
, and
Kaur
,
M.
,
2016
, “
Head Movement-Based Driver Drowsiness Detection: A Review of State-of-Art Techniques
,”
IEEE International Conference on Engineering and Technology
(
ICETECH
), Coimbatore, India, Mar. 17–18, pp.
903
908
.
12.
Li
,
G.
, and
Chung
,
W.-Y.
,
2014
, “
Estimation of Eye Closure Degree Using EEG Sensors and Its Application in Driver Drowsiness Detection
,”
Sensors
,
14
(
9
), pp.
17491
17515
.
13.
Lin
,
C. T.
,
Wu
,
R. C.
,
Liang
,
S. F.
,
Chao
,
W. H.
,
Chen
,
Y. J.
, and
Jung
,
T. P.
,
2005
, “
EEG-Based Drowsiness Estimation for Safety Driving Using Independent Component Analysis
,”
IEEE Trans. Circuits Syst.
,
52
(
12
), pp.
2726
2738
.
14.
Patrick
,
K. C.
,
Imtiaz
,
S. A.
, and
Bowyer
,
S.
,
2016
, “
An Algorithm for Automatic Detection of Drowsiness for Use in Wearable EEG Systems
,”
IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society
(
EMBC
), Orlando, FL, Aug. 16–20, pp.
3523
3526
.
15.
Lin
,
C. T.
,
Chen
,
Y. C.
,
Huang
,
T. Y.
,
Chiu
,
T. T.
,
Ko
,
L. W.
,
Liang
,
S. F.
,
Hsieh
,
H. Y.
,
Hsu
,
S. H.
, and
Duann
,
J. R.
,
2008
, “
Development of Wireless Brain Computer Interface With Embedded Multitask Scheduling and Its Application on Real-Time Driver's Drowsiness Detection and Warning
,”
IEEE Trans. Biomed. Eng.
,
55
(
5
), pp.
1582
1591
.
16.
Chieh
,
T. C.
,
Mustafa
,
M. M.
,
Hussain
,
A.
,
Hendi
,
S. F.
, and
Majlis
,
B. Y.
,
2005
, “
Development of Vehicle Driver Drowsiness Detection System Using Electrooculogram (EOG)
,”
1st International Conference on Computers, Communications, and Signal Processing With Special Track on Biomedical Engineering
(
CCSP
), Kuala Lumpur, Malaysia, Nov. 14–16, pp.
165
168
.
17.
Cifrek
,
M.
,
Medved
,
V.
,
Tonković
,
S.
, and
Ostojić
,
S.
,
2009
, “
Surface EMG Based Muscle Fatigue Evaluation in Biomechanics
,”
Clin. Biomech.
,
24
(
4
), pp.
327
340
.
18.
Maglaveras
,
N.
,
Stamkopoulos
,
T.
,
Diamantaras
,
K.
,
Pappas
,
C.
, and
Strintzis
,
M.
,
1998
, “
ECG Pattern Recognition and Classification Using Non-Linear Transformations and Neural Networks: A Review
,”
Int. J. Med. Inf.
,
52
(
1–3
), pp.
191
208
.
19.
Baek
,
H. J.
,
Chung
,
G. S.
,
Kim
,
K. K.
, and
Park
,
K. S.
,
2012
, “
A Smart Health Monitoring Chair for Nonintrusive Measurement of Biological Signals
,”
IEEE Trans. Inf. Technol. Biomed.
,
16
(
1
), pp.
150
158
.
20.
Lal
,
S. K.
, and
Craig
,
A.
,
2001
, “
A Critical Review of the Psychophysiology of Driver Fatigue
,”
Biol. Psychol.
,
55
(
3
), pp.
173
194
.
21.
Papadelis
,
C.
,
Chen
,
Z.
,
Kourtidou-Papadeli
,
C.
,
Bamidis
,
P. D.
,
Chouvarda
,
I.
,
Bekiaris
,
E.
, and
Maglaveras
,
N.
,
2007
, “
Monitoring Sleepiness With On-Board Electrophysiological Recordings for Preventing Sleep-Deprived Traffic Accidents
,”
Clin. Neurophysiol.
,
118
(
9
), pp.
1906
1922
.
22.
Ma
,
Z.
,
Li
,
B. C.
, and
Yan
,
Z.
,
2016
, “
Wearable Driver Drowsiness Detection Using Electrooculography Signal
,”
IEEE Topical Conference on Wireless Sensors and Sensor Networks
(
WiSNet
), Austin, TX, Jan. 24–27, pp.
41
43
.
23.
Song
,
R.
, and
Tong
,
K.
,
2005
, “
Using Recurrent Artificial Neural Network Model to Estimate Voluntary Elbow Torque in Dynamic Situations
,”
Med. Biol. Eng. Comput.
,
43
(
4
), pp.
473
480
.
24.
Yang
,
G.
,
Lin
,
Y.
, and
Bhattacharya
,
P.
,
2010
, “
A Driver Fatigue Recognition Model Based on Information Fusion and Dynamic Bayesian Network
,”
Inf. Sci.
,
180
(
10
), pp.
1942
1954
.
25.
Hu
,
S.
, and
Zheng
,
G.
,
2009
, “
Driver Drowsiness Detection With Eyelid Related Parameters by Support Vector Machine
,”
Expert Syst. Appl.
,
36
(
4
), pp.
7651
7658
.
26.
Tseng
,
K.-K.
,
Luo
,
J.
,
Hegarty
,
R.
,
Wang
,
W.
, and
Haiting
,
D.
,
2015
, “
Sparse Matrix for ECG Identification With Two-Lead Features
,”
Scientific World J.
,
2015
(
1
), p.
656807
.
27.
Li
,
G.
, and
Chung
,
W.-Y.
,
2013
, “
Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier
,”
Sensors
,
13
(
12
), pp.
16494
16511
.
28.
Matveyeva
,
N.
,
Ivanushkina
,
N.
, and
Ivanko
,
K.
,
2013
, “
Combined Method for Detection of Atrial Late Potentials
,”
IEEE XXXIII International Scientific Conference in Electronics and Nanotechnology
(
ELNANO
), Kiev, Ukraine, Apr. 16–19, pp.
285
289
.
29.
Jammes
,
B.
,
Sharabty
,
H.
, and
Esteve
,
D.
,
2008
, “
Automatic EOG Analysis: A First Step Toward Automatic Drowsiness Scoring During Wake-Sleep Transitions
,”
Somnologie-Schlafforsch. Schlafmedizin
,
12
(
3
), pp.
227
232
.
30.
Sharabaty
,
H.
,
Jammes
,
B.
, and
Esteve
,
D.
,
2008
, “
EEG Analysis Using HHT: One Step Toward Automatic Drowsiness Scoring
,”
22nd International Conference on Advanced Information Networking and Applications-Workshops
(
AINAW
), Gino-wan, Okinawa, Japan, Mar. 25–28, pp.
826
831
.
31.
Castells
,
F.
,
Laguna
,
P.
,
Sörnmo
,
L.
,
Bollmann
,
A.
, and
Roig
,
J. M.
,
2007
, “
Principal Component Analysis in ECG Signal Processing
,”
EURASIP J. Appl. Signal Process.
,
2007
(
1
), p.
074580
.
32.
Barbati
,
G.
,
Porcaro
,
C.
,
Zappasodi
,
F.
,
Rossini
,
P. M.
, and
Tecchio
,
F.
,
2004
, “
Optimization of an Independent Component Analysis Approach for Artifact Identification and Removal in Magnetoencephalographic Signals
,”
Clin. Neurophysiol.
,
115
(
5
), pp.
1220
1232
.
33.
Khushaba
,
R. N.
,
Kodagoda
,
S.
,
Lal
,
S.
, and
Dissanayake
,
G.
,
2011
, “
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm
,”
IEEE Trans. Biomed. Eng.
,
58
(
1
), pp.
121
131
.
34.
Kurt
,
M. B.
,
Sezgin
,
N.
,
Akin
,
M.
,
Kirbas
,
G.
, and
Bayram
,
M.
,
2009
, “
The ANN-Based Computing of Drowsy Level
,”
Expert Syst. Appl.
,
36
(
2
), pp.
2534
2542
.
35.
Deng
,
L. Y.
,
Hsu
,
C. L.
,
Lin
,
T. C.
,
Tuan
,
J. S.
, and
Chang
,
S. M.
,
2010
, “
EOG-Based Human–Computer Interface System Development
,”
Expert Syst. Appl.
,
37
(
4
), pp.
3337
3343
.
36.
MathWorks
,
2016
, “
MATLAB User's Manual
,” MathWorks, Inc., Natick, MA.
37.
Franklin
,
G. F.
,
Powell
,
J. D.
, and
Emami-Naeini
,
A.
,
1988
,
Feedback Control of Dynamic Systems
,
Addison-Wesley
,
Reading, MA
.
38.
Wessberg
,
J.
,
Stambaugh
,
C. R.
,
Kralik
,
J. D.
,
Beck
,
P. D.
,
Laubach
,
M.
,
Chapin
,
J. K.
,
Kim
,
J.
,
Biggs
,
S. J.
,
Srinivasan
,
M. A.
, and
Nicolelis
,
M. A.
,
2000
, “
Real-Time Prediction of Hand Trajectory by Ensembles of Cortical Neurons in Primates
,”
Nature
,
408
(
6810
), pp.
361
365
.
39.
Borst
,
A.
, and
Theunissen
,
F. E.
,
1999
, “
Information Theory and Neural Coding
,”
Nat. Neurosci.
,
2
(
11
), pp.
947
957
.
40.
Priest
,
B.
,
Brichard
,
C.
,
Aubert
,
G.
,
Liistro
,
G.
, and
Rodenstein
,
D. O.
,
2001
, “
Microsleep During a Simplified Maintenance of Wakefulness Test: A Validation Study of the OSLER Test
,”
Am. J. Respir. Crit. Care Med.
,
163
(
7
), pp.
1619
1625
.
41.
Wang
,
Q.
,
Yang
,
J.
,
Ren
,
M.
, and
Zheng
,
Y.
,
2006
, “
Driver Fatigue Detection: A Survey
,”
The Sixth World Congress on Intelligent Control and Automation
(
WCICA
), Dalian, China, June 21–23, pp.
8587
8591
.
42.
Farlex
, “
Facts and Stats
,”
Farlex Inc, Huntingdon
,
PA
, accessed Mar. 1, 2016, http://medical-dictionary.thefreedictionary.com/drowsiness
43.
Shen
,
J.
,
Barbera
,
J.
, and
Shapiro
,
C. M.
,
2006
, “
Distinguishing Sleepiness and Fatigue: Focus on Definition and Measurement
,”
Sleep Med. Rev.
,
10
(
1
), pp.
63
76
.
44.
Csikszentmihalyi
,
M.
, and
Larson
,
R.
,
1987
, “
Validity and Reliability of the Experience-Sampling Method
,”
J. Nerv. Mental Dis.
,
175
, pp.
526
536
.
45.
Lal
,
S. K.
, and
Craig
,
A.
,
2002
, “
Driver Fatigue: Electroencephalography and Psychological Assessment
,”
Psychophysiology
,
39
(
3
), pp.
313
321
.
46.
Vuckovic
,
A.
,
Radivojevic
,
V.
,
Chen
,
A. C.
, and
Popovic
,
D.
,
2002
, “
Automatic Recognition of Alertness and Drowsiness From EEG by an Artificial Neural Network
,”
Med. Eng. Phys.
,
24
(
5
), pp.
349
360
.
47.
Lin
,
C. T.
,
Ko
,
L. W.
,
Chung
,
I.
,
Huang
,
T. Y.
,
Chen
,
Y. C.
,
Jung
,
T. P.
, and
Liang
,
S. F.
,
2006
, “
Adaptive EEG-Based Alertness Estimation System by Using ICA-Based Fuzzy Neural Networks
,”
IEEE Trans. Circuits Syst. I
,
53
(
11
), pp.
2469
2476
.
48.
Yeo
,
M. V.
,
Li
,
X.
,
Shen
,
K.
, and
Wilder-Smith
,
E. P.
,
2009
, “
Can SVM Be Used for Automatic EEG Detection of Drowsiness During Car Driving?
Saf. Sci.
,
47
(
1
), pp.
115
124
.
You do not currently have access to this content.