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Diagnostics
Special Issues
Wearable Sensors and Artificial Intelligence for Ergonomics—2nd Edition
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Wearable Sensors and Artificial Intelligence for Ergonomics—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 953

Special Issue Editors

Dr. Leandro Donisi

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Guest Editor
Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Piazza Luigi Miraglia, 2, 80138 Naples, Italy
Interests: biomedical engineering; biosignal and bioimage processing; ergonomics; rehabilitation engineering, gait analysis, wearable sensors; telemedicine; machine learning; biostatistics
Special Issues, Collections and Topics in MDPI journals
Dr. Carlo Ricciardi

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Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
Interests: machine learning; statistics; gait analysis; health technology assessment; lean six sigma; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Alfonso Maria Ponsiglione

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Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80125 Naples, Italy
Interests: nanomedicine; microfluidics; drug delivery systems; biomaterials
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Cesarelli

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Guest Editor
Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: biomedical engineering; bioengineering; biomedical data analysis; biomedical signal processing; drug delivery systems; biomaterials; polymer microparticles; lean six sigma in healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ergonomics can contribute to maximizing human wellbeing and the efficiency of a working system safeguarding workers’ health. The development of wearable sensors, which are able to collect a wide variety of relevant physiological and environmental parameters, allows for acquiring signals related to the workers in a nonintrusive, automatic and continuous way. Data can be acquired through both custom-made devices (namely, ad hoc ones developed by scientific researchers) and commercial wearable devices. The availability of instruments (such as wearable motion trackers, inertial measurement units, pressure sensors, eye- and face-expression-tracking devices and smart sensors for temperature, breathing, electrocardiography, electroencephalography, electromyography and electrodermal activity) offers a wide perspective for novel solutions in the ergonomic field.

On the other hand, the number of proposed techniques for data processing and analysis increases every day. Newer approaches using deep-learning and classical machine-learning techniques to assess the potential biomechanical risk to which workers are exposed during their work activities are also gaining significant interest in the ergonomic field.

Consequently, this Special Issue (“Wearable Sensors and Artificial Intelligence for Ergonomics—2nd Edition”) aims to both highlight several of the latest developments in the ergonomic/occupational medicine fields and gather proposals which can help delineate a novel emerging branch which considers wearable sensors a tool for biomechanical risk assessment and injury prevention—even through the help of artificial intelligence—during work-, home-, sport- and leisure-related activities. Both research papers and review articles will be considered for publication. We welcome submissions spanning topics across the design of novel sensors and commercial wearable technologies and the development of any novel methodology which aims to integrate quantitative physiological and environmental information—with and without the use of artificial intelligence—as those are the main goals of ergonomics.

Topics of interest include, but are not limited to, the following application fields for machine learning:

  • Ergonomics and occupational medicine
  • Wearable sensors, motion sensors, force/pressure sensors, EMG sensors for ergonomics
  • Sensors for wellbeing
  • Smart clothes and e-textiles for ergonomic applications
  • Activity-monitoring devices and systems
  • Machine learning and deep learning for wearable data analysis
  • Biomechanical risk assessment
  • Health monitoring in working environments
  • Work-related musculoskeletal disorders
  • Novel design approaches for ergonomic assessment
  • mHealth and/or eHealth solutions for ergonomics
  • Data processing applied to risk assessments

Dr. Leandro Donisi
Dr. Carlo Ricciardi
Dr. Alfonso Maria Ponsiglione
Dr. Giuseppe Cesarelli
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. Diagnostics 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

  • medical diagnosis
  • ergonomics
  • occupational medicine
  • biomedical signal processing
  • biomechanics
  • human-activity recognition
  • inertial measurement units and sensors for IoT
  • feature extraction
  • lifting
  • machine learning
  • modeling and simulation
  • neural networks

Published Papers (1 paper)

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Research

15 pages, 2291 KiB  
Article
Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors
by Giuseppe Prisco, Maria Romano, Fabrizio Esposito, Mario Cesarelli, Antonella Santone, Leandro Donisi and Francesco Amato
Diagnostics 2024, 14(6), 576; https://doi.org/10.3390/diagnostics14060576 - 08 Mar 2024
Viewed by 529
Abstract
Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers’ [...] Read more.
Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers’ health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology—based on a single inertial sensor and artificial intelligence—to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field. Full article
(This article belongs to the Special Issue Wearable Sensors and Artificial Intelligence for Ergonomics—2nd Edition)
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