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Sensitivity of heart rate variability as indicator of driver sleepiness
Mahachandra M.a, Yassierlia, Sutalaksana I.Z.a, Suryadi K.a
a Industrial Management Research Group, Faculty of Industrial Technology, Bandung Institute of Technology, 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]A number of research studies have been conducted on interventions to minimize accident risks while driving. Among ergonomic interventions is driver sleepiness detection based on biological signals. However, results seem to be inconclusive. This study investigated the sensitivity of sleepiness detection based on drivers’ heart rate variability (HRV). Sixteen professional male drivers participated in a laboratory experiment using a driving simulator. Heart beat per minute and peak-to-peak heart beat (RR interval) were monitored during sixty minutes driving, along with theta brain wave activity derived from EEG measurements, Heart rate data were then processed in terms of time-domain, frequency-domain, and fractal (Poincaré plot method). Theta activity was used to determine sleepiness event. Finally, hit rates and false alarm rates were calculated for each heart rate measure to find out the sensitivity in detecting sleepiness. Results showed that the decrement of root mean square of successive differences (RMSSD) of RR interval for 28% and the decrement of short-term variability (SD1) in Poincaŕ plot for 27% were the two most sensitive parameters for sleepiness detection. Therefore, these biological signals can be considered in developing sleepiness detection system in the future study. © 2012 IEEE.[/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]Accident risks,Biological signals,Brain wave,Car driver,Driving simulator,Ergonomic intervention,False alarm rate,Frequency domains,Heart beats,Heart rate variability,Heart rates,Hit rate,Laboratory experiments,Peak-to-peak,Poincare,Research studies,Root Mean Square,RR intervals,Sensitive parameter,sleepiness,Sleepiness detection,Theta activity,Time domain[/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]car driver,heart rate variability,simulator,sleepiness[/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][/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/SEANES.2012.6299577[/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]