@article{ART003112149},
author={BANGCHANWOO and Bonghyun Kim},
title={Real-time Fall Accident Prediction using Random Forest in IoT Environment},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2024},
volume={10},
number={4},
pages={27-33}
TY - JOUR
AU - BANGCHANWOO
AU - Bonghyun Kim
TI - Real-time Fall Accident Prediction using Random Forest in IoT Environment
JO - Journal of Internet of Things and Convergence
PY - 2024
VL - 10
IS - 4
PB - The Korea Internet of Things Society
SP - 27
EP - 33
SN - 2466-0078
AB - As of 2023, the number of accident victims in the domestic construction industry is 26,829, ranking second only to other businesses (service industries). The accident types of casualties in all industries were falls (29,229 people), followed by falls (14,357 people). Based on the above data, this study attaches sensors to hard hats and insoles to predict fall accidents that frequently occur at construction sites, and proposes smart safety equipment that applies a random forest algorithm based on the data collected through this. The random forest model can determine fall accidents in real time with high accuracy by generating multiple decision trees and combining the predictions of each tree.
This model classifies whether a worker has had a fall accident and the type of behavior through data collected from the MPU-6050 sensor attached to the hard hat. Fall accidents that are primarily determined from hard hats are secondarily predicted through sensors attached to the insole, thereby increasing prediction accuracy. It is expected that this will enable rapid response in the event of an accident, thereby reducing worker deaths and accidents
KW - IoT;Random Forest;Safety Equipment;Fall Detection;Ensemble Learning
DO -
UR -
ER -
BANGCHANWOO and Bonghyun Kim. (2024). Real-time Fall Accident Prediction using Random Forest in IoT Environment. Journal of Internet of Things and Convergence, 10(4), 27-33.
BANGCHANWOO and Bonghyun Kim. 2024, "Real-time Fall Accident Prediction using Random Forest in IoT Environment", Journal of Internet of Things and Convergence, vol.10, no.4 pp.27-33.
BANGCHANWOO, Bonghyun Kim "Real-time Fall Accident Prediction using Random Forest in IoT Environment" Journal of Internet of Things and Convergence 10.4 pp.27-33 (2024) : 27.
BANGCHANWOO, Bonghyun Kim. Real-time Fall Accident Prediction using Random Forest in IoT Environment. 2024; 10(4), 27-33.
BANGCHANWOO and Bonghyun Kim. "Real-time Fall Accident Prediction using Random Forest in IoT Environment" Journal of Internet of Things and Convergence 10, no.4 (2024) : 27-33.
BANGCHANWOO; Bonghyun Kim. Real-time Fall Accident Prediction using Random Forest in IoT Environment. Journal of Internet of Things and Convergence, 10(4), 27-33.
BANGCHANWOO; Bonghyun Kim. Real-time Fall Accident Prediction using Random Forest in IoT Environment. Journal of Internet of Things and Convergence. 2024; 10(4) 27-33.
BANGCHANWOO, Bonghyun Kim. Real-time Fall Accident Prediction using Random Forest in IoT Environment. 2024; 10(4), 27-33.
BANGCHANWOO and Bonghyun Kim. "Real-time Fall Accident Prediction using Random Forest in IoT Environment" Journal of Internet of Things and Convergence 10, no.4 (2024) : 27-33.
