Characterization of industry 4.0 lean management problem-solving behavioral patterns using EEG sensors and deep learning

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Abstract

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

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Villalba-Diez, J., Zheng, X., Schmidt, D., & Molina, M. (2019). Characterization of industry 4.0 lean management problem-solving behavioral patterns using EEG sensors and deep learning. Sensors (Switzerland), 19(13). https://doi.org/10.3390/s19132841

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