CSafe: An Intelligent Audio Wearable Platform for Improving Construction Worker Safety in Urban Environments
Abstract: Vehicle accidents are one of the greatest cause of death and injury in urban areas for pedestrians, workers, and police alike. In this work, we present CSafe, a low power audio-wearable platform that detects, localizes, and provides alerts about oncoming vehicles to improve construction worker safety. Construction worker safety is a much more challenging problem than general urban or pedestrian safety in that the sound of construction tools can be up to orders of magnitude greater than that of vehicles, making vehicle detection and localization exceptionally difficult. To overcome these challenges, we develop a novel sound source separation algorithm, called Probabilistic Template Matching (PTM), as well as a novel noise filtering architecture to remove loud construction noises from our observed signals. We show that our architecture can improve vehicle detection by up to 12% over other state-of-art source separation algorithms. We integrate PTM and our noise filtering architecture into CSafe and show through a series of real-world experiments that CSafe can achieve up to an 82% vehicle detection rate and a 6.90° mean localization error in acoustically noisy construction site scenarios, which is 16% higher and almost 30° lower than the state-of-art audio wearable safety works.
Loading