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

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, h...

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Bibliographic Details
Published in:Applied sciences 2019-12, Vol.9 (23), p.5066
Main Authors: Chen, Chao-Nan, Liu, Tung-Kuan, Chen, Yenming J.
Format: Article
Language:English
Subjects:
Cognition & reasoning
Data recording
Failure analysis
Human error
Industrial applications
Information processing
Internet of Things
Markov chains
Perception
Petri nets
Safety
Workers
Workflow
Workloads
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9235066