Head Movement-Based Visual Distraction Detection in Cyclists with Earables

Visual distractions among cyclists significantly reduce their situational awareness, increasing the likelihood of accidents. This study introduces the use of an open-source OpenEarable device, equipped with onboard inertial measurement units (IMU), as an easy and non-invasive way to detect visual distractions by measuring head movements linked to such behaviors. Head movement data from 20 participants were collected during natural cycling situations using earable IMU sensors. Both classical machine learning and deep learning techniques are employed to analyze the data to detect visual distractions. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) achieve weighted F1 scores of 0.85 and 0.87, and Cohen's Kappa scores of 0.74 and 0.59, respectively. These findings highlight the potential of earable devices in real-time distraction detection and establish a foundation for future wearable safety systems for cyclists.