Understanding Human Behaviors and Injury Factors in Underground Mines using Data Analytics.

This study aims to understand human behaviors and associated injury causing factors in underground mines using data analytics of historical mining data. Decision tree and association rule were used to provide a statistical analysis of leading factors of hazards in underground mines. Based on the results, we were able to explore hazard feature identification using image feature recognition aiming to provide real-time monitoring for miners to secure healthy and safety operation via wearable computing.

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.