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Smart Sensing Technology for Industry and Environmental Applications

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

Deadline for manuscript submissions: 10 August 2025 | Viewed by 586

Special Issue Editors


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Guest Editor
Construction Technologies Institute, National Research Council of Italy (ITC-CNR), Via Lombardia, 49, 20098 San Giuliano Milanese, MI, Italy
Interests: sustainability; energy efficiency in building; thermal comfort; indoor environmental quality; environmental monitoring systems; supervised machine learning; data analysis; parametric design; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Theoretical and Applied Sciences, Università eCampus, Via Isimbardi 10, 22060 Novedrate, Italy
Interests: measurements; sensors; IR sensors; wearable sensors; thermal comfort; indoor air quality; buildings monitoring; signal processing; data analysis; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart sensing technology enables real-time data collection, analysis and decision making. These advanced sensors, often equipped with AI and machine learning algorithms, can detect changes in the physical environment and provide feedback in real time.

Smart sensors enhance the efficiency, safety and productivity of industry by monitoring the performance of equipment, predicting maintenance needs and preventing costly downtime. They enable the automation of manufacturing processes by allowing machines to communicate with each other to enhance precision and reduce waste. Smart sensors also improve worker safety by detecting hazardous conditions and triggering automatic shutdowns or warnings.

In the environmental field, smart sensor technology plays a crucial role in monitoring the quality of air and water, as well as the environmental quality of the built environment. It is therefore also utilized to assess the impact of climate change. Networks of environmental sensors, for example, can detect pollutants in real time, providing useful data for regulatory compliance and the protection of public health. These systems also enable the continuous remote monitoring of indoor or outdoor environmental quality, contributing to more effective environmental protection measures.

The integration of smart sensor technology into various industries and environmental monitoring systems leads to more sustainable operations, a reduced environmental impact and enhanced resource management. The ability of this technology to process and respond to data in real time makes it a prerequisite for future innovation in both areas. Smart sensor technology integrated into wearables serves as a bridge between the individual and the broader smart sensor ecosystem, improving health, safety and environmental awareness. Equipped with advanced sensors such as accelerometers, gyroscopes, temperature sensors and biometric detectors, wearables can capture various physiological and environmental parameters with high precision.

In the area of health and wellness, wearables help users to optimize their health and detect signs of problems at an early stage.

In industry, wearables can monitor workers’ vital signs and alert them to fatigue or hazardous conditions. They can also track workers' movements in hazardous environments to prevent accidents and elevate their efficiency.

In the field of environmental sensing, wearables can support wider initiatives to control pollution, monitor climate and assess the quality of the build environment.

In this context, this Special Issue welcomes the submission of original papers that address all aspects of smart sensor technology in general, as well as in wearables. The scope of this Special Issue includes, but is not limited to, the following topics:

  • IoT-based monitoring systems;
  • Sensors and sensor networks for smart buildings;
  • Sensors and sensor networks for the optimization of manufacturing processes;
  • Monitoring for BIM and digital twins;
  • Wearables for environmental monitoring;
  • Wearables for industrial applications;
  • Wearables for continuous human health monitoring;
  • ML, or more generally, AI-based approaches for data analysis.

Dr. Francesco Salamone
Dr. Marco Arnesano
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

  • IoT-based monitoring systems
  • sensors and sensor networks for smart buildings
  • sensors and sensor networks for the optimization of manufacturing processes
  • monitoring for BIM and digital twins
  • wearables for environmental monitoring
  • wearables for industrial applications
  • wearables for continuous human health monitoring
  • ML, or more generally, AI-based approaches for data analysis

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Published Papers (1 paper)

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Research

20 pages, 2582 KiB  
Article
Monitoring Construction Workers’ Mental Workload Due to Heat Exposure Using Heart Rate Variability and Eye Movement: A Study on Pipe Workers
by Shiyi He, Dongsheng Qi, Enkai Guo, Liyun Wang, Yewei Ouyang and Lan Zheng
Sensors 2025, 25(8), 2377; https://doi.org/10.3390/s25082377 - 9 Apr 2025
Viewed by 307
Abstract
Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers’ mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe [...] Read more.
Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers’ mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers (n = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results show that applying eight HRV features through the KNN algorithm could obtain the highest classification accuracy of 90.00% (Recall = 0.933, Precision = 0.875, F1 = 0.903, AUC = 0.887). This study could provide a new perspective for monitoring the mental workload of construction workers, and it could also provide a feasible approach for the construction industry to monitor workers’ mental workload in hot conditions. Full article
(This article belongs to the Special Issue Smart Sensing Technology for Industry and Environmental Applications)
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