Premium
Slow eye movement detection can prevent sleep‐related accidents effectively in a simulated driving task
Author(s) -
SHIN DUK,
SAKAI HIROYUKI,
UCHIYAMA YUJI
Publication year - 2011
Publication title -
journal of sleep research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 117
eISSN - 1365-2869
pISSN - 0962-1105
DOI - 10.1111/j.1365-2869.2010.00891.x
Subject(s) - eye movement , task (project management) , sleep (system call) , audiology , simulation , computer science , driving simulator , psychology , artificial intelligence , medicine , engineering , systems engineering , operating system
Summary A delayed response caused by sleepiness can result in severe car accidents. Previous studies suggest that slow eye movement (SEM) is a sleep‐onset index related to delayed response. This study was undertaken to verify that SEM detection is effective for preventing sleep‐related accidents. We propose a real‐time detection algorithm of SEM based on feature‐extracted parameters of electrooculogram (EOG), i.e. amplitude and mean velocity of eye movement. In Experiment 1, 12 participants (33.5 ± 7.3 years) performed an auditory detection task with EOG measurement to determine the threshold parameters of the proposed algorithm. Consequently, the valid threshold parameters were determined, respectively, as >5° and <30° s −1 . In Experiment 2, 11 participants (32.8 ± 7.2 years) performed a simulated car‐following task to verify that the SEM detection is effective for preventing sleep‐related accidents. Accidents in the SEM condition were significantly more numerous than in the non‐SEM condition ( P < 0.01, one‐way repeated‐measures anova followed by Scheffé’s test). Furthermore, no accident occurred in the SEM condition with a warning generated using the proposed algorithm. Results also demonstrate clearly that the SEM detection can prevent sleep‐related accidents effectively in this simulated driving task.