IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER

Authors

  • A.D. Jeyarani Professor, AnnaSaheb Dange College of Engineering & Technology, Maharashtra
  • Reena Daphne Assistant Professor, in Nagercoil, Tamil Nadu
  • Chettiyar Vani Vivekanand Research Scholar, Anna University, Chennai

DOI:

https://doi.org/10.24297/jac.v13i9.5804

Keywords:

Drowsiness detection, Electroencephalogram (EEG), Electrooculogram (EOG), Mean Comparison test.

Abstract

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series .

 

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Author Biographies

A.D. Jeyarani, Professor, AnnaSaheb Dange College of Engineering & Technology, Maharashtra

Department of Electronics and Telecommunication Engineering

Reena Daphne, Assistant Professor, in Nagercoil, Tamil Nadu

Department of Electrical and Electronics, Stella Mary’s College of Engineering,

Chettiyar Vani Vivekanand, Research Scholar, Anna University, Chennai

Department of Information and Communication Engineering,

Additional Files

Published

2017-02-22

How to Cite

Jeyarani, A., Daphne, R., & Vani Vivekanand, C. (2017). IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER. JOURNAL OF ADVANCES IN CHEMISTRY, 13(9), 6489–6502. https://doi.org/10.24297/jac.v13i9.5804

Issue

Section

Articles