A physiological measures-based method for detecting inattention in drivers using machine learning approach

• Autorzy: Sahayadhas A. , Sundaraj K. , Murugappan M. , Palaniappan R.

Identyfikatory

DOI

10.1016/j.bbe.2014.12.002

• Języki publikacji: EN

Abstrakty

EN

Background: In recent years, as a result of the usage of electronic gadgets in vehicles, driver inattention has become one of the major causes of road accidents that lead to severe physical injuries, deaths and significant economic losses. Statistics ensure the need of a reliable driver inattention detection system that can alert the driver before a mishap happens. Methods: In this work, we aimed to develop a system that can detect inattention using electrocardiogram (ECG) and surface electromyogram (sEMG) signals. Cognitive and visual inattention was manipulated by asking the driver to respond to phone calls and short messaging services, respectively. A total of 15 male subjects participated in the data collection process. The subjects were asked to drive for two hours in a simulated environment at three different times of the day. ECG, sEMG and video were obtained throughout the experiment. The gathered physiological signals were preprocessed to remove noises and artefacts. The inattention features were extracted from the preprocessed signals using conventional statistical, higher-order statistical and higher-order spectral features. The features were classified using k-nearest neighbour analysis, linear discriminant analysis and quadratic discriminant analysis. Results: The bispectral features gave overall maximum accuracies of 98.12% and 90.97% for the ECG and EMG signals, respectively. Conclusion: We conclude that ECG and EMG signals can be explored further to develop a robust and reliable inattention detection system.

Słowa kluczowe

EN

ECG; EMG; physiological measures; driver inattention; driver distraction

PL

sygnał EKG; sygnał EMG; pomiar fizjologiczny; nieuwaga kierowcy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2015
• Tom: Vol. 35, no. 3
• Strony: 198--205
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Sahayadhas A.

• Department of Computer Science Engineering, Vels University, Chennai, Tamil Nadu, India

autor

Sundaraj K.

• AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Perlis, Malaysia

autor

Murugappan M.

• AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Perlis, Malaysia

autor

Palaniappan R.

• AI-Rehab Research Group, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, Perlis, Malaysia

Bibliografia

• [1] NHTSA. An examination of driver distraction as recorded in NHTSA databases. NHTSA's National Center for Statistics and Analysis; 2009.
• [2] Cohen JT, Graham JD. A revised economic analysis of restrictions on the use of cell phones while driving. Risk Anal 2003;23.
• [3] Klauer SG, Dingus TA, Neale VL, Sudweeks JD, Ramsey D. The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data; 2006.
• [4] UNESCAP. ESCAP works towards reducing poverty and managing globalization. Transp Commun Bull Asia Pac 2009;79.
• [5] Hedlund J, Simpson H, Mayhew D.International conference on distracted driving. Summary of proceedings and recommendations; 2006.
• [6] Ranney TA, Garrott EMAR, Goodman MJ. NHTSA driver distraction research: past, present, and future; 2000.
• [7] Harbluk JL, Noy YI, Trbovich PL, Eizenman M. An on-road assessment of cognitive distraction: impacts on drivers' visual behavior and braking performance. Accid Anal Prev 2007;39:372–9.
• [8] Itoh M. Individual differences in effects of secondary cognitive activity during driving on temperature at the nose tip; 2009;7–11.
• [9] Avinash W, Dvijesh S, Ioannis P. A novel method to monitor driver's distractions. Atlanta, GA, USA: ACM; 2010.
• [10] Benedetto S, Pedrotti M, Minin L, Baccino T, Re A, Montanari R. Driver workload and eye blink duration. Transp Res F Traffic Psychol Behav 2011;14:199–208.
• [11] Östlund J, Nilsson L, Carsten O, Merat N, Jamson H, Jamson S, et al. In: Östlund J, Carsten O, Jamson S, editors. Deliverable 2—HMI and safety-related driver performance. 2004.
• [12] Laberge J, Scialfa C, White C, Caird J. Effects of passenger and cellular phone conversations on driver distraction. Transp Res Rec J Transp Res Board 2004;1899:109–16.
• [13] Kawakita E, Itoh M, Oguri K. Estimation of driver's mental workload using visual information and heart rate variability; 2010;765–9.
• [14] Monkaresi H, Calvo RA, Hong Y. A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J Biomed Health Inf 2014;18:1153–60.
• [15] Mizuno A, Okumura H, Matsumura M. In: Magjarevic R, editor. Development of neckband mounted active bio-electrodes for non-restraint lead method of ECG R wave. Berlin/Heidelberg: Springer; 2009. pp. 1394–7.
• [16] Moriguchi A, Otsuka A, Kohara K, Mikami H, Katahira K, Tsunetoshi T, et al. Spectral change in heart rate variability in response to mental arithmetic before and after the beta-adrenoceptor blocker, carteolol. Clin Auton Res 1992;2: 267–70.
• [17] De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. Crit Rev Biomed Eng 1984;11:251–79.
• [18] Whitham EM, Lewis T, Pope KJ, Fitzgibbon SP, Clark CR, Loveless S, et al. Thinking activates EMG in scalp electrical recordings. Clin Neurophysiol 2008;119:1166–75.
• [19] Malmo RB, Malmo HP. On electromyographic (EMG) gradients and movement-related brain activity: significance for motor control, cognitive functions, and certain psychopathologies. Int J Psychophysiol 2000;38: 143–207.
• [20] Thiffault P, Bergeron J. Monotony of road environment and driver fatigue: a simulator study. Accid Anal Prev 2003;35:381–91.
• [21] Sahayadhas A, Sundaraj K, Murugappan M. Drowsiness detection during different times of day using multiple features. Australas Phys Eng Sci Med 2013;36:243–50.
• [22] Kim WC, Fabozzi FJ, Cheridito P, Fox C. Controlling portfolio skewness and kurtosis without directly optimizing third and fourth moments. Econ Lett 2014;122:154–8.
• [23] Annenkov SY, Shrira VI. Evaluation of skewness and kurtosis of wind waves parameterized by JONSWAP spectra. J Phys Oceanogr 2014;44:1582–94.
• [24] Nikias CL. Higher-order spectral analysis. IEEE; 1993. p. 319.
• [25] Zhou S-M, Gan JQ, Sepulveda F. Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface. Inf Sci 2008;178: 1629–40.
• [26] Bhattacharyya S, Khasnobish A, Chatterjee S, Konar A, Tibarewala DN. Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data, Kharagpur; 2010;126–31.
• [27] Howden WE, Wieand B. QDA – a method for systematic informal program analysis. IEEE Trans Softw Eng 1994;20:445–62.
• [28] Juszczak P, Tax DMJ, Verzakov S, Duin RPW. Domain based LDA and QDA. Hong Kong: Baptist University; 2006. pp. 788–91.
• [29] Miyaji M, Kawanaka H, Oguri K. Driver's cognitive distraction detection using physiological features by the adaboost; 2009;1–6.
• [30] Engström J, Johansson E, Östlund J. Effects of visual and cognitive load in real and simulated motorway driving. Transp Res F Traffic Psychol Behav 2005;8:97–120.
• [31] Yu L, Sun X, Zhang K. In: Rau P, editor. Driving distraction analysis by ECG signals: an entropy analysis internationalization, design and global development. Berlin/Heidelberg: Springer; 2011. pp. 258–64.
• [32] Kurt MB, Sezgin N, Akin M, Kirbas G, Bayram M. The ANN-based computing of drowsy level. Expert Syst Appl 2009;36:2534–42.
• [33] Hu S, Zheng G. Driver drowsiness detection with eyelid related parameters by support vector machine. Expert Syst Appl 2009;36:7651–8.
• [34] Dziuda Ł, Biernacki MP, Baran PM, Truszczyński OE. The effects of simulated fog and motion on simulator sickness in a driving simulator and the duration of after-effects. Appl Ergon 2014;45:406–12.
• [35] Yu X. Real-time nonintrusive detection of driver drowsiness. University of Minnesota, Duluth report; 2009.
• [36] Gomez-Clapers J, Casanella R. A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. IEEE Sens J 2012;12:610–6.
• [37] Hyun Jae B, Gih Sung C, Ko Keun K, Kwang Suk P. A smart health monitoring chair for nonintrusive measurement of biological signals. IEEE Trans Inf Technol Biomed 2012;16:150–8.