@inproceedings{662f9e0329c04817a3d17a2a21af159b,
title = "Near-miss accident detection for ironworkers using inertial measurement unit sensors",
abstract = "In the construction industry, fall accidents are the leading cause of construction-related fatalities; in particular, ironworkers have the highest risk of fatal accidents. Detecting near-miss accidents for ironworkers provides crucial information for interrupting and preventing the precursors of fall accidents while simultaneously addressing the problem of sparse accident data for ironworkers' fall-risk assessments. However, current methods for detecting near-miss accidents are based upon workers' self-reporting, which introduces variability to the collected data. This paper aims to present a method that uses Inertial Measurement Unit (IMU) sensor data to automatically detect near-miss accidents during ironworkers' walking motion. Then, using a Primal Laplacian Support Vector Machine, a developed semi-supervised algorithm trains a system to predict near-miss incidents using this data. The accuracy of this semi-supervised algorithm was measured with different metrics to assess the impact of the automated near-miss incident detection in construction worksites. The experimental validation of the algorithm indicates that near-miss incidents may be estimated and classified with considerable accuracy-above 98 percent. Then the computational burden of the proposed algorithm was compared with a One-Class Support Vector Machine (OC-SVM). Based upon the proposed detection approach, high-risk actions in the construction site can be detected efficiently, and steps towards reducing or eliminating them may be taken.",
keywords = "Inertial Measurement Unit sensor, Near-misss, Sensing and Communication, Worker safety",
author = "Aria, {Sepideh S.} and Kanghyeok Yang and Ahn, {Changbum R.} and Vuran, {Mehmet C.}",
year = "2014",
doi = "10.22260/isarc2014/0115",
language = "English (US)",
series = "31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings",
publisher = "University of Technology Sydney",
pages = "854--859",
editor = "Quang Ha and Xuesong Shen and Ali Akbarnezhad",
booktitle = "31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings",
note = "31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 ; Conference date: 09-07-2014 Through 11-07-2014",
}