NDLI: Driver Distraction Recognition Using Wearable IMU Sensor Data

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Driver Distraction Recognition Using Wearable IMU Sensor Data

Content Provider	MDPI
Author	Sun, Wencai Si, Yihao Guo, Mengzhu Li, Shiwu
Copyright Year	2021
Description	Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.
Starting Page	1342
e-ISSN	20711050
DOI	10.3390/su13031342
Journal	Sustainability
Issue Number	3
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2021-01-28
Access Restriction	Open
Subject Keyword	Sustainability Transportation Science and Technology Traffic Safety Manual Distraction Dynamic Time Warping Wearable Inertial Measurement Units Hidden Markov Model
Content Type	Text
Resource Type	Article

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