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Wearable Sensors and Technologies in Ergonomics, and Occupational Safety &...
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Wearable Sensors and Technologies in Ergonomics, and Occupational Safety & Health

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 12359

Special Issue Editors

Dr. Mohammad Iman Mokhlespour Esfahani

E-Mail Website
Guest Editor
Worldwide Design and Engineering, Amazon, Seattle, WA 98170, USA
Interests: wearable technology; smart textiles; biomechanics; ergonomics; human factors engineering; machine learning
Dr. Sol Lim

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, University of Arizona, Tucson, AZ 85721, USA
Interests: ergonomics and human factors; wearable technology; predictive modeling; occupational health and safety; healthcare ergonomics; biomechanics in disability and inclusive design
Special Issues, Collections and Topics in MDPI journals
Dr. Reza Akhavian

E-Mail Website
Guest Editor
Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA
Interests: construction robotics; artificial intelligence (AI); Internet of Things (IoT); data analytics; machine learning; cyber-physical systems; building information modeling (BIM); construction automation; digital transformation; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable devices have been used in several domains including medical, sports, fitness, rehabilitation, fashion, military, gaming, and entertainment. However, the use of wearable devices for enhanced occupational health, safety, and ergonomics continue to capture the attention of scientists and engineers. Indeed, many researchers are developing these types of innovative medical devices, several of which are reaching the commercial market.  The main aim of this Special Issue is to seek high-quality research studies that highlight the recent developments and applications, and address the challenges of wearable sensors/technologies at the workplace. The topics of interest include, but are not limited to:

  • The development, assessment, and application of wearable sensors/technologies in ergonomics.
  • The development, assessment, and application of wearable sensors/technologies in occupational safety and health.
  • The application of wearable sensors for measuring physiological parameters in the workplace.
  • The application of wearable sensors for measuring physical parameters in the workplace.
  • The role of wearable sensors and technologies in decreasing musculoskeletal disorders.
  • The development and applications of exoskeletons in ergonomics.
  • Usability studies related to the application of wearable sensors and technologies in the workplace.
  • Smart personal protective equipment (PPE) in the workplace.
  • Concerns and challenges with using wearable sensors and technologies in the workplace.
  • The market and acceptability of wearable devices in the workplace.

Dr. Mohammad Iman Mokhlespour Esfahani
Dr. Sol Lim
Dr. Reza Akhavian
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. Electronics 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 2400 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

  • wearable technology
  • wearable sensor
  • smart garments
  • smart textiles
  • exoskeleton
  • ergonomics
  • human factors engineering
  • occupational safety and health
  • smart personal protective equipment (PPE)
  • usability

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Published Papers (2 papers)

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Research

17 pages, 2157 KiB  
Article
Challenging Ergonomics Risks with Smart Wearable Extension Sensors
by Nikola Maksimović, Milan Čabarkapa, Marko Tanasković and Dragan Randjelović
Electronics 2022, 11(20), 3395; https://doi.org/10.3390/electronics11203395 - 20 Oct 2022
Cited by 2 | Viewed by 2457
Abstract
Concerning occupational safety, the aim of ergonomics as a scientific discipline is to study and adjust working conditions, worker equipment, and work processes from a psychological, physiological, and anatomical aspect instead of adapting the worker to the needs of the job. This paper [...] Read more.
Concerning occupational safety, the aim of ergonomics as a scientific discipline is to study and adjust working conditions, worker equipment, and work processes from a psychological, physiological, and anatomical aspect instead of adapting the worker to the needs of the job. This paper will discuss and analyze the potential of the garment-embedded body posture tracking sensor and its usage as standard working equipment, which is meant to help correct improper and high-risk upper body positions during prolonged and static work activities. The analysis evaluation cross-reference is based on the Rapid Upper Limb Assessment ergonomics risk assessment tool. Signals generated by the wearable are meant to help the wearer and observer promptly-continuously detect and correct bad posture. The results show a positive progression of workers’ body posture to reduce the ergonomic risks this research covers. It can be concluded that wearable technology and sensors would significantly contribute to the observer as the evaluation tool and the wearer to spot the risk factors promptly and self-correct them independently. This feature would help workers learn and improve the correct habits of correcting ergonomically incorrect body postures when performing work tasks. Full article
(This article belongs to the Special Issue Wearable Sensors and Technologies in Ergonomics, and Occupational Safety & Health)
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14 pages, 1776 KiB  
Article
Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning
by Srimantha E. Mudiyanselage, Phuong Hoang Dat Nguyen, Mohammad Sadra Rajabi and Reza Akhavian
Electronics 2021, 10(20), 2558; https://doi.org/10.3390/electronics10202558 - 19 Oct 2021
Cited by 83 | Viewed by 7264
Abstract
Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may [...] Read more.
Manual material handling tasks have the potential to be highly unsafe from an ergonomic viewpoint. Safety inspections to monitor body postures can help mitigate ergonomic risks of material handling. However, the real effect of awkward muscle movements, strains, and excessive forces that may result in an injury may not be identified by external cues. This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling. The analysis utilized a lifting equation developed by the U.S. National Institute for Occupational Safety and Health (NIOSH). This equation determines a Recommended Weight Limit, which suggests the maximum acceptable weight that a healthy worker can lift and carry, as well as a Lifting Index value to assess the risk extent. Four different machine learning models, namely Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest are developed to classify the risk assessments calculated based on the NIOSH lifting equation. The sensitivity of the models to various parameters is also evaluated to find the best performance using each algorithm. Results indicate that Decision Tree models have the potential to predict the risk level with close to 99.35% accuracy. Full article
(This article belongs to the Special Issue Wearable Sensors and Technologies in Ergonomics, and Occupational Safety & Health)
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