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In this paper authors are going to present a Markov Decision Process (MDP)based algorithm in Industrial Internet of Things (IIoT) as a safety compliancelayer for human in loop system. Though some industries are moving towardsIndustry 4.0 and attempting to automate the systems as much as possible byusing robots, still human in loop systems are very common in developingcountries like India. When ever there is a need for human machine interaction,there is a scope of health hazard. In this work we have developed a system forone such industry using MDP. The proposed algorithm used in this system learnedthe probability of state transition from experience as well as the system isadaptable to new changes by incorporating the concept of transfer learning. Thesystem was evaluated on the data set obtained from 39 sensors connected to acomputer numerically controlled (CNC) machine pushing data every second in a24x7 scenario. The state changes are typically instructed by a human whichsubsequently lead to some intentional or unintentional mistakes and errors. Theproposed system raises an alarm for the operator to warn which he may or maynot overlook depending on his own perception about the present condition of thesystem. Repeated ignorance of the operator for a particular state transitionwarning guides the system to retrain the model. We observed 95.61% alarmsraised by the said system are taken care of by the operator. 3.2% alarms arecoming from the changes in the system which in turn used to retrain the modeland 1.19% alarms are false alarms. We could not compute the error coming fromthe mistake performed by the human operator as there is no ground truthavailable for that.

AI based Safety System for Employees of Manufacturing Industries in Developing Countries