Workers’ context description and unsafe behavior recognition in Internet of things for mines - Shimin FENG; Zhongyu LIU; Xiao YU; Lei MENG; Zhikai ZHAO; Enjie DING (物联网学报)
The mine is a complicated environment.The intelligent recognition of such behavior requires not only the data-driven activity recognition method,but also the machine-readable domain knowledge based approach.However,the data of IoT for mines lacks the semantic information.Besides,there is no standard way of describing the worker’s context and representing the knowledge of worker’s unsafe behavior.In order to solve the above problems,a semantic ontology based approach to describing the worker’s context and a hybrid method based framework for worker’s unsafe behavior recognition were presented.This method combines the data-driven approach and the knowledge-driven approach.Firstly,an introduction to the semantic ontology in the Internet of things was given.Then,the importance and necessity of worker’s unsafe behavior recognition was introduced.After that,the research background on human activity recognition,context-awareness and semantic ontology was presented.This was followed by the semantic ontology based approach to the worker’s context description.Based on the context modeling,the framework that combined the data-driven method and the knowledge-driven method for the worker’s unsafe behavior recognition was proposed.The application of the framework was illustrated with the recognition of a kind of worker’s unsafe behavior who don’t wear the protective and safety equipment.Finally,the conclusions were drawn and the prospect of using the artificial intelligence method in the application layer of mine IoT was presented.