TY - JOUR

T1 - Operator EYEVP: Operator Dataset for Fatigue Detection Based on Eye Movements, Heart Rate Data, and Video Information

AU - Коваленко, Светлана

AU - Мамонов, Антон

AU - Кузнецов, Владислав

AU - Булыгин, Александр

AU - Шошина, Ирина Ивановна

AU - Брак, Иван

AU - Кашевник, Алексей

PY - 2023/7/6

Y1 - 2023/7/6

N2 - Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physiological and objective behavioral indicators. To develop an effective model of fatigue detection, it is important to record a dataset with people in a state of fatigue as well as in a normal state. We carried out data collection using an eye tracker, a video camera, a stage camera, and a heart rate monitor to record a different kind of signal to analyze them. In our proposed dataset, 10 participants took part in the experiment and recorded data 3 times a day for 8 days. They performed different types of activity (choice reaction time, reading, correction test Landolt rings, playing Tetris), imitating everyday tasks. Our dataset is useful for studying fatigue and finding indicators of its manifestation. We have analyzed datasets that have public access to find the best for this task. Each of them contains data of eye movements and other types of data. We evaluated each of them to determine their suitability for fatigue studies, but none of them fully fit the fatigue detection task. We evaluated the recorded dataset by calculating the correspondences between eye-tracking data and CRT (choice reaction time) that show the presence of fatigue.

AB - Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physiological and objective behavioral indicators. To develop an effective model of fatigue detection, it is important to record a dataset with people in a state of fatigue as well as in a normal state. We carried out data collection using an eye tracker, a video camera, a stage camera, and a heart rate monitor to record a different kind of signal to analyze them. In our proposed dataset, 10 participants took part in the experiment and recorded data 3 times a day for 8 days. They performed different types of activity (choice reaction time, reading, correction test Landolt rings, playing Tetris), imitating everyday tasks. Our dataset is useful for studying fatigue and finding indicators of its manifestation. We have analyzed datasets that have public access to find the best for this task. Each of them contains data of eye movements and other types of data. We evaluated each of them to determine their suitability for fatigue studies, but none of them fully fit the fatigue detection task. We evaluated the recorded dataset by calculating the correspondences between eye-tracking data and CRT (choice reaction time) that show the presence of fatigue.

KW - утомление

KW - Eye Movements

KW - Head Movements/physiology

KW - Heart Rate

KW - Humans

KW - Reaction Time

KW - Videotape Recording

KW - face and head video

KW - fatigue

KW - HRV (heart rate variability)

KW - gaze tracking

KW - eye tracking

KW - dataset

UR - https://www.mendeley.com/catalogue/e82d5608-5514-32d0-85f5-cd76991338c5/

U2 - 10.3390/s23136197

DO - 10.3390/s23136197

M3 - Article

C2 - 37448047

VL - 23

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 13

M1 - 6197

ER -