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Sensors for Smart Manufacturing: Advanced Sensing Solutions for Human-Centric, Inclusive and Resilient Industrial Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 467

Special Issue Editors


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Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Interests: sensor monitoring; sustainable manufacturing; machine learning; cyber physical systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via della Vasca Navale 79, 00146 Rome, Italy
Interests: advanced manufacturing technology and systems; smart manufacturing; intelligent sensor monitoring; cloud manufacturing; digital factory technologies

Special Issue Information

Dear Colleagues,

High-quality submissions are solicited for this Special Issue that focuses on the integration of advanced sensor technologies into smart manufacturing systems. The objective is to compile interdisciplinary studies that investigate innovative sensing solutions for human-centric, inclusive, and resilient industrial systems. This includes novel sensor designs, such as wearable and nearable systems, for monitoring and optimizing cognitive load, stress, and operator well-being in industrial environments. Emphasis is also placed on developing robust sensor fusion techniques and machine learning models for the real-time assessment of mental workload and stress levels. Contributions addressing the integration of digital twin frameworks for predictive maintenance and process optimization in manufacturing settings are also welcomed. Research topics may include novel sensor fabrication methods, ergonomic and inclusive design strategies that account for diverse operator needs, and quantitative models for cognitive and stress evaluation. In addition, system-level evaluations demonstrating enhancements in production efficiency, quality control, and workplace safety are encouraged. This Special Issue aims to foster research that bridges advanced sensor technology with manufacturing applications while ensuring that industrial environments not only achieve high performance but also support operator health, reduce cognitive burden, and promote inclusivity.

This issue is aligned with the scope of Sensors, addressing the design, development, and application of sensor-based systems in industrial environments. The focus on practical sensor fusion, digital-twin integration, and machine learning for the real-time monitoring of cognitive and physiological parameters in manufacturing contributes to the field of precision measurements and advanced data analytics. The research examples sought—such as experimental studies on wearable sensor systems for operator monitoring and quantitative evaluations of human–machine interfaces—demonstrate both the underlying measurement science and the tangible technological applications that meet Sensors’ emphasis on innovative sensor development and practical impact.

Dr. Alessandro Simeone
Dr. Alessandra Caggiano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart manufacturing
  • wearable sensors
  • nearable sensors
  • cognitive load modeling
  • stress assessment
  • operator well-being
  • ergonomics
  • digital twins
  • sensor fusion
  • inclusive design

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Published Papers (1 paper)

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Research

23 pages, 2544 KiB  
Article
Fuzzy-Based Sensor Fusion for Cognitive Load Assessment in Inclusive Manufacturing Strategies
by Agnese Testa, Alessandro Simeone, Massimiliano Zecca, Andrea Paoli and Luca Settineri
Sensors 2025, 25(11), 3356; https://doi.org/10.3390/s25113356 - 27 May 2025
Viewed by 390
Abstract
In recent years, the need to design inclusive workplaces has grown, particularly in manufacturing contexts where high cognitive demands may disadvantage neurodiverse individuals. In manufacturing environments, neurodiverse workers often experience difficulties processing standard instructions, increasing cognitive load and errors and reducing overall performance. [...] Read more.
In recent years, the need to design inclusive workplaces has grown, particularly in manufacturing contexts where high cognitive demands may disadvantage neurodiverse individuals. In manufacturing environments, neurodiverse workers often experience difficulties processing standard instructions, increasing cognitive load and errors and reducing overall performance. This study proposes a methodology to assess cognitive load during assembly tasks to support workers with dyslexia. A multi-layer fuzzy logic framework was developed, integrating physiological, environmental, and task-related data. Physiological signals, including heart rate, heart rate variability, electrodermal activity, and eye-tracking data, were collected using wearable sensors. Ambient conditions were also measured. The model emphasizes the Reading dimension of cognitive load, critical for dyslexic individuals challenged by text-based instructions. A controlled laboratory study with 18 neurotypical participants simulated dyslexia scenarios with and without support, compared to a control condition. Results indicated that a lack of support increased cognitive load and reduced performance in complex tasks. In simpler tasks, control participants showed higher cognitive effort, possibly employing overcompensation strategies by exerting additional cognitive resources to maintain performance. Support mechanisms, such as audio prompts, effectively reduced cognitive load, highlighting the framework’s potential for fostering inclusive practices in industrial environments. Full article
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