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Real-time Fall Accident Prediction using Random Forest in IoT Environment

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2024, 10(4), pp.27-33
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : July 3, 2024
  • Accepted : August 14, 2024
  • Published : August 31, 2024

BANGCHANWOO 1 Bonghyun Kim 1

1서원대학교

Accredited

ABSTRACT

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

Citation status

* References for papers published after 2023 are currently being built.