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Open AccessArticle

Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

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Ph. D. Program in Management and in Engineering Science and Technology, College of Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan
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Department of Mechanical and Automation Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan
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Department of Logistics Management, National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5066; https://doi.org/10.3390/app9235066
Received: 26 August 2019 / Revised: 11 November 2019 / Accepted: 19 November 2019 / Published: 24 November 2019
This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data. As part of Industry 4.0, a human operator possesses various characteristics, but cannot be consistently understood as well as a machine. Thus, human-machine interaction plays an important role. This study focuses on incumbent challenges on the basis of estimated mental states. Given the operation logs from data recording hardware, a Hidden Markov model on top of a human cognitive model was trained to capture a production line worker’s sequential faults. Our study found that retaining workers’ attention is insufficient and tracking the state of perception is key to accomplishing production tasks. A safe workflow policy requires attention and perception. Accordingly, our proposed Petri Net enhances operation safety and improves production efficiency. View Full-Text
Keywords: Information processing model; hidden Markov chain; petri net; human–machine interaction Information processing model; hidden Markov chain; petri net; human–machine interaction
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Chen, C.-N.; Liu, T.-K.; Chen, Y.J. Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net. Appl. Sci. 2019, 9, 5066.

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