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Fatigue Classification of Ocular Indicators using Support Vector Machine
Puspasari M.A.a, Iridiastadi H.a, Sutalaksana I.Z.a, Sjafruddin A.a
a Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, Jawa Barat, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEE.Fatigue is one of major causes of road accident. Time on task is one of factors that worsen fatigue, however, previous literatures limited their study into short simulated driving. Moreover, drivers in Indonesia frequently experience long duration driving caused by high traffic density. This study aims to determine fatigue classification based on ocular indicators in long duration driving condition. Classification of fatigue was conducted using Support Vector Machine (SVM). Twelve subjects participated in this study, and they were told to drive for three straight hours by driving simulator. Results showed improvements of blink duration, blink rate, PERCLOS, and microsleep by the end of three hours driving. Deterioration of saccadic velocity, saccadic amplitude, and pupil diameter were also occurred by the end of three hours driving. Results from Spearman rho suggest blink duration, PERCLOS, and microsleep as parameters that significantly correlated to KSS score. Radial basis function (RBF) was used as Kernel function since it has the highest accuracy compared to linear functions. SVM model indicated validity of seven ocular indicators as fatigue classification, with accuracy above 80%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Driving conditions,Driving simulator,Fatigue classifications,Radial Basis Function(RBF),road driving,Saccadic velocity,Simulated driving,Traffic densities[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]classification,fatigue,ocular indicators,road driving,Support Vector Machine[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Authors would like to acknowledge RSKE ITB for designing study protocol and experimental guidance. Authors also thanked Ergonomics Centre Universitas Indonesia for devices used in this study. This study was funded by doctoral research grant from The Ministries of Research. Technology, and Higher Education Republic of Indonesia (Hibah Penelitian Disertasi Doktor).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICIIBMS.2018.8549999[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]