Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Wakeup
T2 - 21st Saudi Computer Society National Computer Conference, NCC 2018
AU - Alotaibi, Ahad Shabib
AU - Asif, Amna
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/12/27
Y1 - 2018/12/27
N2 - This paper aims to improve warning signals of the existing drowsiness detection systems in terms of efficiency and effectiveness. For this purpose, firstly, we exploit visual, audio and tactile modalities to discover the most suitable signal for alerting the car drivers in drowsiness detection systems. Secondly, the resultant signals are merged into various combinations to form multimodal warnings. From this, we got four different combinations. Furthermore, we have done an experimental evaluation in order to identify the most effective and efficient multimodal warnings for drowsiness detection systems. Results from our observational studies showed that the most suitable unimodal signals are as follows: (1) 40 ms on and 50 ms off the flashlight of smartphone for visual warning, (2) Apple iOS Ringtones-Alarm for the audio warning, and (3) 1 pulse in a second with 500 ms inter-stimulus interval for the vibrotactile warning. From the experimental study, we found that the most efficient and preferable warning for alerting the drowsy driver is multimodal signal consisting of visual, audio, and tactile modalities. This paper is helpful for the car industries to develop the effective warnings of drowsiness detection systems, furthermore, it helps in adopting these systems and in preventing accidents.
AB - This paper aims to improve warning signals of the existing drowsiness detection systems in terms of efficiency and effectiveness. For this purpose, firstly, we exploit visual, audio and tactile modalities to discover the most suitable signal for alerting the car drivers in drowsiness detection systems. Secondly, the resultant signals are merged into various combinations to form multimodal warnings. From this, we got four different combinations. Furthermore, we have done an experimental evaluation in order to identify the most effective and efficient multimodal warnings for drowsiness detection systems. Results from our observational studies showed that the most suitable unimodal signals are as follows: (1) 40 ms on and 50 ms off the flashlight of smartphone for visual warning, (2) Apple iOS Ringtones-Alarm for the audio warning, and (3) 1 pulse in a second with 500 ms inter-stimulus interval for the vibrotactile warning. From the experimental study, we found that the most efficient and preferable warning for alerting the drowsy driver is multimodal signal consisting of visual, audio, and tactile modalities. This paper is helpful for the car industries to develop the effective warnings of drowsiness detection systems, furthermore, it helps in adopting these systems and in preventing accidents.
KW - Drowsy detection systems
KW - warnings, multimodal interfaces, human-computer interaction
U2 - 10.1109/NCG.2018.8593003
DO - 10.1109/NCG.2018.8593003
M3 - Conference contribution/Paper
AN - SCOPUS:85061502999
T3 - 21st Saudi Computer Society National Computer Conference, NCC 2018
BT - 21st Saudi Computer Society National Computer Conference, NCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 April 2018 through 26 April 2018
ER -