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      Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces

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          Abstract

          Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.

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          A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

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            A review of the meanings and the implications of the Industry 4.0 concept

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              Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

              The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                03 April 2020
                April 2020
                : 20
                : 7
                : 2011
                Affiliations
                [1 ]Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain; shengjing.sun@ 123456alumnos.upm.es (S.S.); xiaochen.zheng@ 123456epfl.ch (X.Z.); j.gparedes@ 123456alumnos.upm.es (J.G.P.)
                [2 ]Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [3 ]ICT for Sustainable Manufacturing, SCI-STI-DK, École polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
                [4 ]Jülich Supercomputing Center, Forschungszentrum Jülich GmbH, Wilhelm-Wohnen-Str, 52428 Jülich, Germany; b.gong@ 123456fz-juelich.de
                Author notes
                [* ]Correspondence: j.ordieres@ 123456upm.es
                Author information
                https://orcid.org/0000-0002-8790-7371
                https://orcid.org/0000-0003-1506-3314
                https://orcid.org/0000-0001-7770-2738
                https://orcid.org/0000-0002-3548-7387
                https://orcid.org/0000-0002-9677-6764
                Article
                sensors-20-02011
                10.3390/s20072011
                7180548
                32260123
                659486e3-6c46-4820-88b2-efe78924e4b5
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 March 2020
                : 01 April 2020
                Categories
                Article

                Biomedical engineering
                healthy operator 4.0,human–cyber–physical system,industrial internet of things,industry 4.0,smart workplaces

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