Edge Computing With Complementary Capsule Networks for Mental State Detection in Underground Mining Industry

Abstract

Most safety accidents are caused by human factor in underground resource mining industry. This is because the non-uniform lighted and noisy and dangerous environment easily evokes the negative mental state and results in nonstandard production operation. This paper proposes an edge computing mental state framework of the internet of things in the underground mining industry. Moreover, a filtering algorithm using a defined threshold function is developed. Furthermore, a complemented capsule network model is constructed by using two residual modules. In addition, a two-stage mental state fusion algorithm is proposed with EEG signals and facial expression. At last, the mental state variation characteristics are explored with the illuminating and coloring. Experiments show that the mental state detection accuracy is increased by 2.6%. The higher mental arousal is at the illumination between 320 Lx and 330 Lx.