ArticleSensors (Basel, Switzerland)2019
Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Quantum cyber-physical systems.Scientific reports · 2022Article
- Human-Machine Integration in Processes within Industry 4.0 Management.Sensors (Basel, Switzerland) · 2021Article
- Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.Frontiers in human neuroscience · 2021Review
- Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One.Sensors (Basel, Switzerland) · 2020Article
- Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems.Sensors (Basel, Switzerland) · 2020Article
- Impact of industry 4.0 to create advancements in orthopaedics.Journal of clinical orthopaedics and trauma · 2020Article
- Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning in Physiological Signal Data: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber-Physical Complex Networks.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.