Head Mounted IMU-Based Driver’s Gaze Zone Estimation Using Machine Learning Algorithm

AbstractPredicting the driver’s gaze could be important information in preventing accidents while driving. In this study, machine learning models for estimating the driver’s gaze distraction through head movement data were created and their performance was compared and evaluated. Participants wore glasses-type eye trackers and performed the task of selecting the touch screen buttons while driving. The input variable used in the model was data obtained from a 3-axis accelerometer sensor and a 3-axis gyroscope sensor, and the target variable was eye-gaze data. As a result, it was confirmed that the gaze area could be estimated with a precision, sensitivity, specificity, and F1-score of 72.1%, 72.5%, 66.0%, and 69.3%, respectively, only with the head movement sensing data. The model trained using time-series datasets had higher performance than using non-time series datasets. This study presented one alternative that could be used to determine the driver’s status with an inexpensive sensor.Keywords: Gaze zonehead mounted IMUdriver monitoringmachine learning AcknowledgementThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1A2C4002641).Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used in this study are available upon request from the corresponding author.Additional informationNotes on contributorsMungyeong ChoeMungyeong Choe is a Ph.D. student majoring in the Grado Department of Industrial and Systems Engineering with a concentration in human factors at Virginia Tech. Her research focuses on the effects of empathic in-vehicle agents in driving contexts.Yeongcheol ChoiYeongchoel Choi received a B.S. degree in Industrial and Management Engineering from Incheon National University. He is working in the artificial intelligence technology team at POSCO DX as a senior researcher. His research interests include three-dimensional localization using computer vision.Jaehyun ParkJaehyun Park is currently an Associate Professor with the Department of Industrial and Management Engineering, Incheon National University (INU). His research interests include semantic network analysis, machine/deep learning on physical behavior, and computational cognitive engineering.Jungyoon KimJungyoon Kim is currently an Assistant Professor with the Department of Computer Science, Kent State University, USA. His areas of experience and expertise are smart health and wellbeing, physiological sensor design, and infrastructure and intelligent decision supports, sleep-related breathing disorder prediction, environmental monitoring and assessment.