Integration between artificial intelligence technologies for miners' unsafe behavior identification

doi:10.16265/j.cnki.issn1003-3033.2019.01.002

Abstract

Abstract: A fusion method, for identifying unsafe behavior of miners, was worked out as a result of integration between three existing artificial intelligence identification methods including the computer vision based on depth learning, depth image representing depth information and wearable sensor. The method uses PCA to reduce the dimensions of the behavior features extracted by the three recognition techniques, and classifies the features by support vector machine (SVM). Data on miners' fall behavior were used as positive samples and data on five kinds of daily behavior including walking, sitting down, bending, squatting and lying down were used as negative samples. Three artificial intelligence identification methods and the fusion method were applied to identify the fall behavior of miners. The results show that the effectiveness of the fusion method in recognizing unsafe behavior is higher than that of the three artificial intelligence methods.

Key words: unsafe behavior, intelligent identification, principal component analysis (PCA), behavior characteristics, falling test

CLC Number:

X936

TONG Ruipeng, ZHANG Yanwei. Integration between artificial intelligence technologies for miners' unsafe behavior identification[J]. China Safety Science Journal, 2019, 29(1): 7-12.

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