Accident Prediction Model Using Environmental Sensors for Industrial Internet of Things
Jung-Hyok Kwon, Eui‐Jik Kim · Sensors and Materials · 2019
We present an accident prediction model using environmental sensors for industrial Internet of Things (IIoT), with the aim of preventing various accidents that occur at construction sites.The model is expressed as association rules generated by analyzing data collected from environmental sensors that periodically measure the changes in their surrounding environment.To develop the prediction model, we conduct the following three steps: preprocessing, association rule generation, and visualization.In the preprocessing step, the continuous value within the dataset is converted into the categorical value.In the association rule generation step, the association rules used for the prediction model are generated to represent the relationship between the accident types and causes.Finally, in the visualization step, the generated association rules are visualized in the form of a matrix plot and network graph.To demonstrate the accident prediction model, we performed an experimental implementation using open-source R. The results show that the generated association rules enable the prediction of various accidents including heatstroke, asphyxiation, collapse, and fire on the basis of the environmental factors of the construction site.