  Handheld phone distraction is the leading cause of traffic accidents.
However, few efforts have been devoted to detecting when the phone distraction
happens, which is a critical input for taking immediate safety measures. This
work proposes a phone-use monitoring system, which detects the start of the
driver's handheld phone use and eliminates the distraction at once.
Specifically, the proposed system emits periodic ultrasonic pulses to sense if
the phone is being held in hand or placed on support surfaces (e.g., seat and
cup holder) by capturing the unique signal interference resulted from the
contact object's damping, reflection and refraction. We derive the short-time
Fourier transform from the microphone data to describe such impacts and develop
a CNN-based binary classifier to discriminate the phone use between the
handheld and the handsfree status. Additionally, we design an adaptive
window-based filter to correct the classification errors and identify each
handheld phone distraction instance, including its start, end, and duration.
Extensive experiments with fourteen people, three phones and two car models
show that our system achieves 99% accuracy of recognizing handheld phone-use
instances and 0.76-second median error to estimate the distraction's start
time.
