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

Abstract

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.

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

“…Despite Petri Net's graphical and mathematical potential, various authors explore how to make the language more flexible, compatible and unambiguous (Billington et al , 2003). In the context of the Fourth Industrial Revolution, Petri Net has a strong relationship with “HUMAN-MACHINE-INTERACTION,” “SIMULATION-AND-ANALYSIS,” “STOCHASTIC-MODELS” and “INFORMATION-PROCESSING” (Figure 7 (b)) and in smart environments it has helped researchers to model processes in real-time (Lee, 2020), human–machine safety interaction (Chen et al , 2019), automated planning for business process (Marrella, 2019) and collaborative industrial workflow (Du et al , 2018). In this sense, while studies relating BPMN and I4.0 are focused on process changeability and business development, Petri Net has a stronger relationship with information and communication technologies, issues of human and machine interaction, and control of activities in real time.…”

Accuracy of Hidden Markov Models in Identifying Alterations in Movement Patterns during Biceps-Curl Weight-Lifting Exercise

“…In HMMs, the state's hidden sequence can be estimated based on the visible observation sequence [40] and characterized by five elements in a symbolic manner [18,41], where N represents the number of states in the model. For the variable q t , there is a corresponding state value, represented by S = {S 1 , S 2 , .…”

Challenges in Application of Petri Nets in Manufacturing Systems

“…Development of modern complex production processes for Industry 4.0 can be supported by code generation for Arduino [33]. Petri nets can also enhance the human-machine safety interaction and improve production efficiency [34].…”