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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 23  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SS, MSS 2024-01-18
11:05
Ishikawa
(Primary: On-site, Secondary: Online)
Preliminary Study on Driver Drowsiness Detection with Deep Learning Using Vehicular Data
Yutaro Nakagama, Daisuke Ishii (JAIST), Kazuki Yoshizoe (Kyushu Univ.) MSS2023-63 SS2023-42
Driver drowsiness detection (DDD) systems are being developed to prevent accidents caused by the distraction of automobi... [more] MSS2023-63 SS2023-42
pp.64-69
SeMI, IPSJ-ITS, IPSJ-MBL, IPSJ-DPS 2023-05-19
16:35
Okinawa Okinawa Institute of Science and Technology (OIST)
(Primary: On-site, Secondary: Online)
Towards Improving the Generalization Performance of a Face Texture Analysis AI System for Predicting Driver Drowsiness
Tomoya Kubo, Tomoyuki Yokogawa, Kazutami arimoto, Masaki Hokari (Okayama Prefectural Univ.), Isao Kayano (Kawasaki University of Medical) SeMI2023-20
We are currently developing a Face Texture Analysis AI system that predicts driver drowsiness based on the overall impre... [more] SeMI2023-20
pp.94-98
MBE 2023-05-13
13:00
Nagano Shinshu University Estimation of Drowsiness While Driving Using Autonomic Nervous System-related Indicators Based on Photoplethysmogram
Shu Kanda, Makoto Abe (Shinshu Univ.) MBE2023-1
The occurrence of drowsiness while driving is a social problem because it causes accidents. Therefore, a method to asses... [more] MBE2023-1
pp.1-6
MBE 2022-05-13
13:30
Online Online Development of a Simulator for Evaluation Driver Drowsiness during Automated Driving
Satsuki Moriyama, Takashi Imamura, Yutaka Nakajima, Shigehito Tanahashi (Niigata Univ.) MBE2022-1
In recent years, automated driving technology has been developed, which is expected to provide many benefits such as red... [more] MBE2022-1
pp.1-5
MBE, NC
(Joint)
2022-03-03
10:20
Online Online Proposal of drowsiness detection from multimodal biosignals using t-SNE
Takumi Nomura, Shin'ichiro Kanoh (SIT) MBE2021-94
The method to detect drowsiness from electroencephalogram (EEG), electrocardiogram (ECG), and electroretinogram (EOG) wh... [more] MBE2021-94
pp.31-34
HCGSYMPO
(2nd)
2021-12-15
- 2021-12-17
Online Online Drowsiness prevention using a social robot
Koki Hara, Atsushi Nakazawa (Kyoto Univ.), Ayumi Takemoto (Univ. of Latvia)
In this paper, we show a potential to use a social robot to prevent participants from drowsiness and to keep them awaki... [more]
MBE 2019-07-20
11:20
Tokushima   Near future drowsiness prediction using support vector regression analysis of electroencephalogram parameters
Izzat Akbar, Arthur Rumagit, Mitaku Utsunomiya, Takamasa Morie, Tomohiko Igasaki (Kumamoto Univ.) MBE2019-21
Traffic accident becomes a global concern recently. One of the causes is drowsy driving. Previous studies tried to solve... [more] MBE2019-21
pp.13-18
ITS, IEE-ITS 2019-03-04
14:35
Kyoto Kyoto Univ. Comparison between Driving Simulator and Actual Car as Train Data for Drowsiness Level Estimation
Mikuni Motoi, Haruki Kawanaka, Koji Oguri (Aichi Prefectual Unv.) ITS2018-90
As one of the studies of drowsiness estimation, estimation of drowsiness using only information of a drive recorder has ... [more] ITS2018-90
pp.35-39
CPM, ED, EID, SDM, ICD, MRIS, QIT, SCE, OME, EMD
(Joint) [detail]
2018-03-08
15:40
Shizuoka   Smart Steering Wheel with Swept Frequency Capacitive Sensing
Yutaro Ono, Soto Oki, Yuhei Morimoto, Reiji Hattori (Kyushu Univ.), Masayuki Watanabe, Nanae Michida, Hironobu Yonezawa, Daichi Sato, Kazuo Nishikawa (Mazda) EMD2017-76 MR2017-47 SCE2017-47 EID2017-49 ED2017-121 CPM2017-141 SDM2017-121 ICD2017-126 OME2017-70
We present a smart steering wheel that detects the gripping position and area, as well as the distance to the approachin... [more] EMD2017-76 MR2017-47 SCE2017-47 EID2017-49 ED2017-121 CPM2017-141 SDM2017-121 ICD2017-126 OME2017-70
pp.21-23
ITS, IE, ITE-MMS, ITE-HI, ITE-ME, ITE-AIT [detail] 2018-02-15
10:45
Hokkaido Hokkaido Univ. Estimation Algorithm Independent of Road Structure in Drowsiness Level Estimation from Event Data Recorder
Yuki Kaneko, Haruki Kawanaka, Koji Oguri (Aichi Prefectural Univ.) ITS2017-64 IE2017-96
The studies of drowsiness level estimation are mainly used biomedical signals.
But it is not realistic from the point o... [more]
ITS2017-64 IE2017-96
pp.17-22
MBE, NC, NLP
(Joint)
2018-01-26
15:50
Fukuoka Kyushu Institute of Technology Drowsiness assessment by gazing/blinking parameter from EOG in driving simulator environment
Arthur Mourits Rumagit, Izzat Aulia Akbar, Mitaku Utsunomiya, Takamasa Morie, Tomohiko Igasaki (Kumamoto Univ.) MBE2017-67
Many works related to drowsy driving have been conducted to prevent the traffic accident caused by drowsy driving. In th... [more] MBE2017-67
pp.7-11
MBE, NC
(Joint)
2017-03-14
14:35
Tokyo Kikai-Shinko-Kaikan Bldg. Discrimination of drowsiness level based on facial skin thermogram using CNN
Hiroko Adachi, Kosuke Oiwa, Akio Nozawa (AGU) MBE2016-99
The relation between drowsiness and facial expression can be classified into five levels according to NEDO method which ... [more] MBE2016-99
pp.83-86
MBE, NC
(Joint)
2017-03-14
15:00
Tokyo Kikai-Shinko-Kaikan Bldg. Development of feedback system for heat diffusion control
Wataru Takaoka, Shizuka Bando, Kosuke Oiwa (AGU), Hirotoshi Asano (Kagawa Univ.), Akio Nozawa (AGU) MBE2016-100
The objective of this study was to develop a biofeedback system for drowsiness prevention. Drowsiness is related to hea... [more] MBE2016-100
pp.87-90
MBE 2017-01-28
13:00
Fukuoka Kyushu University Drowsiness evaluation using gazing time information under driving simulator environment
Mitaku Utsunomiya, Arthur Mourits Rumagit, Izzat Aulia Akbar, Tomohiko Igasaki (Kumamoto Univ) MBE2016-78
According to the several studies, it was shown that the majority of traffic accidents were caused by dozing driving. In ... [more] MBE2016-78
pp.47-51
EST 2016-09-09
14:45
Okinawa Ishigakishi-Shoko-Kaikan Dependence of the cardiac action potential on the human body structure in the driver drowsiness detection system
Shunsuke Ishizuka, Kengo Sugahara, Shima Okada (Kinki Univ) EST2016-55
We are now focusing on developing a driver drowsiness detection system with the cardiac action potentials on the human b... [more] EST2016-55
pp.283-288
PRMU, IPSJ-CVIM, MVE [detail] 2016-01-21
09:30
Osaka   Influence of gaze feedback on arousal level in viewing a movie recorded by a car-mounted camera
Shinichi Hikita (Osaka Electro-Communication Univ.), Hiroyuki Hoshino (Toyota CRDL) PRMU2015-116 MVE2015-38
We propose a new method for car drivers to help maintain alertness, which is based on a visual feedback of eye-gaze moti... [more] PRMU2015-116 MVE2015-38
pp.37-40
MBE 2014-01-25
13:20
Saga Saga University Relationship between driving drowsiness and eye-blink -- Examination of parameter extraction from EOG --
Yuki Yae, Norihito Enmoto, Tomohiko Igasaki, Nobuki Murayama, Zhencheng Hu (Kumamoto Univ) MBE2013-111
It is widely known that many traffic accidents occur every year not only in Japan but also in the world. Drowsiness or s... [more] MBE2013-111
pp.85-88
SSS 2013-09-26
15:00
Tokyo Kikai-Shinko-Kaikan Bldg. [Invited Talk] Preventing commercial vehicle accident caused by driver fatigue.
Kazuya Suzuki (ISL) SSS2013-19
Issues concerning preventing accidents caused by driver fatigue are discusses.
1) Current status in Japan and its backg... [more]
SSS2013-19
pp.19-22
SSS 2012-05-24
13:40
Tokyo Kikai-Shinko-Kaikan Bldg. Mutual Relationships between Effects of Driver Drowsiness on Physiological indices, Driving Performances, and Subjective Evaluations
Ryo Ishikawa, Makoto Itoh, Toshiyuki Inagaki (Univ. of Tsukuba) SSS2012-2
Despite enormous efforts of research on the detection of drowsy driving, no single method dominates the others. All of t... [more] SSS2012-2
pp.7-12
MBE, NC
(Joint)
2012-03-16
11:05
Tokyo Tamagawa University Real-Time Evaluation of Arousal Level Based on Saccadic Eye Movement on a Movie from a Car-Mounted View
Tsuyoshi Itaoka, Shinichi Hikita, Takahiko Ono, Yasuhide Kobayashi, Mitsuyuki Saito (Hiroshima City Univ.) MBE2011-138
In order to evaluate arousal level in real-time, the characteristics of eyelid motion and saccadic eye movement while vi... [more] MBE2011-138
pp.167-170
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