Utilizing mobile phone sensors and machine learning to detect drivers through right leg motion

his paper begins with the assumption that a mobile phone is placed in the right trouser pocket. Its primary objective is to ascertain whether the mobile phone belongs to the driver or the passenger. To achieve this goal, the paper conducts an analysis of raw data collected from phone sensors, specifically the accelerometer, gyroscope, orientation, and GPS, in order to identify and detect movement in the right leg. Based on this analysis, the application makes the decision to disconnect the signal and Internet access on the driver's phone if the car's speed exceeds 60 miles per hour, while maintaining these services for passengers. Subsequently, relevant features are extracted from the data, and a variety of machine learning algorithms are evaluated to determine the most suitable model for the task. The results demonstrate promising performance, suggesting that the proposed method could serve as an effective tool for detecting distracted driving.