Analysis and development of a novel algorithm for the in-vehicle hand-usage of a smartphone

TL;DR

This paper presents a self-contained, frequency-domain analysis method using smartphone IMU data and SVM classification to detect in-vehicle phone usage, aiming to improve road safety by identifying dangerous driver behaviors.

Contribution

It introduces a novel frequency-based feature extraction technique from smartphone IMU data for driver phone usage detection, independent of external inputs.

Findings

High detection accuracy demonstrated on naturalistic driving data

Effective differentiation between phone use and vehicle motion

Self-contained approach suitable for real-world applications

Abstract

Smartphone usage while driving is unanimously considered to be a really dangerous habit due to strong correlation with road accidents. In this paper, the problem of detecting whether the driver is using the phone during a trip is addressed. To do this, high-frequency data from the triaxial inertial measurement unit (IMU) integrated in almost all modern phone is processed without relying on external inputs so as to provide a self-contained approach. By resorting to a frequency-domain analysis, it is possible to extract from the raw signals the useful information needed to detect when the driver is using the phone, without being affected by the effects that vehicle motion has on the same signals. The selected features are used to train a Support Vector Machine (SVM) algorithm. The performance of the proposed approach are analyzed and tested on experimental data collected during mixed naturalistic driving scenarios, proving the effectiveness of the proposed approach.