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Ergonomics for New Technology and Digital Health

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (7 April 2023) | Viewed by 2826

Special Issue Editor


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Guest Editor
Industrial and Management Systems Engineering, College of Engineering, Kyung Hee University, Yongin 17104, Korea
Interests: human factors; ergonomics; usability; patien safety; user modeling

Special Issue Information

Dear Colleagues,

With the development of various sensors and digital technologies, new digital products such as bio-signal measuring equipment and VR/AR equipment are being developed. However, the implementation and application of these new technologies as well as the maximization of value-added or utility through ergonomic application of these technologies are also very important. Research on ergonomic applications and evaluation techniques for products using these technologies is essential for the application of technologies that are user-friendly and have maximized usability and utility. In addition, with the development of various sensor technologies, it has become easier to collect information for health care or data on various characteristics of the body. However, in order to effectively utilize application services or healthcare using this, it is also necessary to develop techniques to identify and predict the characteristics of health and body using these data. The goal of this Special Issue is to contribute to the effective product development and technology application of these state-of-the-art technologies by utilizing various ergonomic methodologies.

We are seeking theoretical, methodological, empirical, case, field, and multidisciplinary studies on different aspects of ergonomic R&D that include, but are not limited to:

  • Ergonomic optimization for a variety of new technologies;
  • Product design and evaluation methods for the application of various new technologies;
  • Usability evaluation of product concepts with new technology without physical substance;
  • Ergonomic application of new technologies and bio-signal data for health care. 

Prof. Dr. Sangwoo Bahn
Guest Editor

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Keywords

  • ergonomic design
  • ergonomic evaluation
  • usability
  • usability evaluation
  • healthcare
  • new technology
  • concept evaluation

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Published Papers (1 paper)

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Research

20 pages, 691 KiB  
Article
Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning
by Pradeep Kumar Hanumegowda and Sakthivel Gnanasekaran
Int. J. Environ. Res. Public Health 2022, 19(22), 15179; https://doi.org/10.3390/ijerph192215179 - 17 Nov 2022
Cited by 8 | Viewed by 2463
Abstract
A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic [...] Read more.
A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers. Full article
(This article belongs to the Special Issue Ergonomics for New Technology and Digital Health)
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