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Ocular indicators as fatigue detection instruments for Indonesian drivers
Puspasari M.A.a, Iridiastadi H.a, Sutalaksana I.Z.a, Sjafruddin A.a
a Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, 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]© 2019 KIIE.Fatigue is a well-known major cause of road accidents. Ocular indicators have been regarded as reliable indicators for measuring fatigue. However, results from previous investigations remain unclear about the performance of ocular parameters to detect fatigue in a real-time driving condition. This study was aimed at evaluating performance of several responsive ocular measures to detect fatigue during a simulated driving task. Thirteen participants drove a medium fidelity driving simulator continuously for 3 hours in high and low traffic density, after normal sleep duration (8 h) and sleep-deprived condition (4 h). Results from the present study showed that sleep deprivation substantially affects blink duration, percentage of eye closure (PERCLOS), microsleep, slow eye movement (SEM), and saccadic parameters. Traffic density, however, only had moderate effect toward ocular parameters. Among all ocular indicators, blink duration, PERCLOS, and saccadic PV demonstrated high accuracy, sensitivity, and specificity to detect fatigue. The present study suggests that blink duration has the highest performance to detect low-level fatigue and heavy fatigue, with a cut-off value of 285.17 ms and 512.31 ms, respectively, compared to other ocular indicators. The implications of this study are implementing a fatigue detection device based on blink duration, PERCLOS, and saccadic PV parameters.[/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][/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]Fatigue Detection,Ocular Indicators,Simulated Driving,Sleep Deprivation,Traffic Density[/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]The present study was conducted with support from the research grant of the Directorate of Higher Education (DIKTI). The authors acknowledge Work System Engineering and Ergonomics Laboratory of the Bandung Institute of Technology for data processing and thank Ergonomics Centre Universitas Indonesia for providing the instruments for this experiment.[/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/10.7232/iems.2019.18.4.748[/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]