Driver Distraction Recognition Using Wearable IMU Sensor Data

Wencai Sun, Yihao Si, Mengzhu Guo and Shiwu Li
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Wencai Sun: School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China
Yihao Si: School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China
Mengzhu Guo: School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China
Shiwu Li: School of Transportation, Jilin University, 5988 Renmin Street, Changchun 130022, China

Sustainability, 2021, vol. 13, issue 3, 1-17

Abstract: 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.

Keywords: traffic safety; manual distraction; Dynamic Time Warping; wearable inertial measurement units; Hidden Markov Model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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