Multi-classifier for highly reliable driver drowsiness detection in android platform

Biomedical Engineering - Applications, Basis and Communications

Volumn 24, Issue 2, 2012, Pages 147-154

  Lee, Boon Giin ^a   Chung, Wan Young ^a  

^a Pukyong National University   (South Korea)

Author keywords

Android based smartphone; Attributes fusion; Fatigue; Multi classifier

Indexed keywords

CAPTURE DRIVERS; DISCRETE METHOD; DRIVER BEHAVIOR; DRIVER DROWSINESS; DRIVER FATIGUE; DYNAMIC BAYESIAN NETWORKS; FACIAL IMAGES; MONITORING SYSTEM; MULTI-CLASSIFIER; SENSORY INFORMATION; SMALL SCALE; STEERING WHEEL; SYSTEM TESTING;

EYE MOVEMENTS; FATIGUE OF MATERIALS; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; SIGNAL ENCODING; SMARTPHONES; SUPPORT VECTOR MACHINES;

ROBOTS;

ANDROID; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLASSIFIER; COMPUTER SYSTEM; DISEASE CLASSIFICATION; DROWSINESS; DYNAMIC BAYESIAN NETWORK; EVALUATION; EYE MOVEMENT; FATIGUE; GENETIC ALGORITHM; INDEPENDENT COMPONENT ANALYSIS; MOBILE PHONE; PHOTOELECTRIC PLETHYSMOGRAPHY; RELIABILITY; SENSOR; SUPPORT VECTOR MACHINE;

EID: 84861800193     PISSN: 10162372     EISSN: None     Source Type: Journal    
DOI: 10.4015/S1016237212500159     Document Type: Article

References (14)

1. Murata A, Hiramatsu Y, Evaluation of drowsiness by HRV measures -basic study for drowsy driver detection, Proc of 4th Int Workshop on Comput Intell and Appl, Hiroshima Univ, Japan, pp. 99-102, 2008.
2. Yao KP, Lin WH, Fang CY, Wang JM, Chang SL, Chen SW, Real-time vision-based driver drowsiness/fatigue detection system, IEEE 71st Vehicular Technol Conf, Taipei, Taiwan, pp. 1-5, 2010.
3. Yang JH, Mao ZH, Tijerina L, Pilutti T, Coughlin JF, Feron E, Detection of driver fatigue caused by sleep deprivation, IEEE Trans Syst Man Cybern A, Syst Humans 39: 697, 2009.
4. Helene TV, Thierry B, Serge B, Matti K, Jouko V, Evangelos B, Maria P, Johan E, Anders A, Development of a driver situation assessment module in the AIDE project, Proc of 16th IFAC World Congress, Czech Republic, 2005.
5. Achro HD. Available: http://huins.com/en/m12.php?m=rd&no=100.
6. PC100. Available: http://huins.com/en/m12.php?m=rd&no=101.
7. Shin HS, Jung SJ, Kim JJ, Chung WY, Real time car driver's condition monitoring system, Proc IEEE Sens 2010, Hawaii, USA, pp. 951-954, 2010.
8. Kuncheva, LI, Whitaker CJ, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning 51: 181, 2003.
9. Sharkey AJ, Sharkey AJ, Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems, Springer-Verlag New York, Inc., 1999.
10. Zaharim A, Sharum, Abdullah, Ibrahim MD, Nopiah ZM, Genetic algorithm in time series fatigue analysis, Euro J of Sci Res 28: 68, 2009.
11. Ji Q, Lan P, Looney C, A probabilistic framework for modeling and real-time monitoring human fatigue, IEEE Trans Syst Man Cybern A, Syst Humans 36: 862, 2006.
12. Estimation of drivers' drowsiness level using a neural network based error correcting output coding' method, IEEE Conf on Intell Transp Syst, Funchal, Madeira Island, Portugal, pp. 1887-1892, 2010.
13. Hu SY, Zheng GT, Driver drowsiness detection with eyelid related parameters by support vector machine, Expert Syst with Appl 36: 7651, 2009.
14. Lin CT, Wu RC, Liang SF, Chao WH, Chen YJ, Jung TP, EEG-based drowsiness estimation for safety driving using independent component analysis, IEEE Trans Circuits Syst I: Regular Papers 52: 2726, 2005.