Driver Distraction Recognition Using Wearable IMU Sensor Data

• Main Authors: Wencai Sun, Yihao Si, Mengzhu Guo, Shiwu Li
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Sustainability
• Subjects: traffic safety; manual distraction; Dynamic Time Warping; wearable inertial measurement units; Hidden Markov Model
• Online Access: https://www.mdpi.com/2071-1050/13/3/1342

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.