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Sensorized Devices and Technologies for Occupational Risk Assessment and Prevention

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

Deadline for manuscript submissions: closed (25 March 2023) | Viewed by 3855

Special Issue Editors


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Guest Editor
Grupo de Investigación en Instrumentación y Acústica Aplicada, Departamento de Telemática y Electrónica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: wearable technology; Internet of Things; occupational risk prevention
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, 28031 Madrid, Spain
Interests: applied acoustics; wearable sensors; Parkinson's disease; noise control; industrial applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal
Interests: human factors and ergonomics; occupational safety; hygiene; safety engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the number of devices with sensors built in is growing fast. These devices are used in popular consumer electronics and in professional devices for a variety of applications. Currently, reducing work-related injuries and illnesses is an open challenge. Thanks to recent technological advances, several technologies have opened up new and exciting opportunities for occupational risk assessment and prevention. Portable sensorized devices enable the continuous monitoring of posture, movement, noise, light, temperature, chemical concentrations, vital signs, etc., which can be used as diagnostic tools for occupational risk prevention.

The early prevention of situations of occupational risk before they occur helps to reduce the impact of accidents and illnesses in our lives. Currently, the technology opens a wide range of solutions; devices such as smartphones, tablets, or wearables (e.g., smartwatches, smart wristbands) are used to manage specific occupational health problems. The combination of the Internet of Things (IoT), big data (collection and analysis of large amounts of data), and smart working environments (SWE) provides an opportunity for monitoring activities conducted by the worker, machinery, and tools in order to provide safer occupational environments.

These technologies, however, also come with their own challenges, such as their reliability, validity, and feasibility within occupational risk-assessment contexts, and the challenging interpretability of the data to effectively inform prevention practice.

Additionally, the resulting synergies should also be considered in managing occupational risk prevention, such as prevention enhancement, accident and illness reduction, productivity improvement, cost reduction, availability, and sharing of information, data privacy, quasi-real-time decision making, as well as integration with the concepts of digitization of industry (Industry 4.0).

This Special Issue aims to highlight the most recent research regarding wearables for occupational risk assessment and prevention, sensor-based technics for accident and illness reduction, and more generally, sensor-based methods and technics to obtain safer occupational environments.

Contributions that address, but are not restricted to, the following topics are welcome:
• Wearable sensors;
• Reliability and validity of sensor-based measurements;
• Sensor-based feedback on motor performance, motion and activity detection;
• Recognition of work-related musculoskeletal disorders by sensors and/or wearable devices;
• Fall detection systems;
• Innovative applications for monitoring occupational safety and health risk factors;
• Smart clothing/textiles technologies for monitoring occupational safety and health risk factors;
• Innovative smartphone applications for monitoring occupational safety and health risk factors;
• Wearable technology for physical, chemical, and/or biological exposure monitoring;
• Body sensor networks.

Dr. Juan Manuel López
Dr. Ignacio Pavón García
Dr. Nélson Costa
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

  • occupational risk prevention (ORP)
  • wearables or wearable technology
  • Internet of Things (IoT)
  • big data
  • smart work environments (SWE)
  • Industry 4.0
  • digitalization of occupational safety and health (OH+S)

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Published Papers (1 paper)

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Research

13 pages, 1794 KiB  
Article
Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters
by Denisse Bustos, Filipa Cardoso, Manoel Rios, Mário Vaz, Joana Guedes, José Torres Costa, João Santos Baptista and Ricardo J. Fernandes
Sensors 2023, 23(1), 194; https://doi.org/10.3390/s23010194 - 24 Dec 2022
Cited by 8 | Viewed by 3453
Abstract
Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. [...] Read more.
Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions. Full article
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