JOURNAL ARTICLE

Defining a systems framework for characterizing physical work demands with wearable sensors.

  • Published In: Annals of Work Exposures & Health, 2024, v. 68, n. 5. P. 443 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Stirling, Leia; Acosta-Sojo, Yadrianna; Dennerlein, Jack T 3 of 3

Abstract

This article focuses on a systems-thinking framework for measuring the physical demands of work using advanced wearable sensor technologies, such as inertial measurement units (IMUs), heart rate monitors, and electromyography (EMG). It reviews current methods for assessing physical job demands—including self-report, observational, and direct measurement tools—and highlights limitations in precision and contextual relevance. The proposed framework, adapted from the International Classification of Functioning, Disability and Health (ICF), organizes physical demands across five domains: function, activities, tasks, jobs, and work, and aligns these with three categories of decision-making: design and safety management, individual assessment and training, and evidence-based policy formation. The article emphasizes the importance of selecting appropriate metrics and sensor applications based on specific decision needs, stakeholder input, and ethical considerations, while acknowledging ongoing challenges in metric validation, data interpretation, and privacy. This approach aims to enhance understanding of evolving work demands and improve worker safety, health, and well-being through more precise and contextually informed measurement strategies.

Additional Information

  • Source:Annals of Work Exposures & Health. 2024/06, Vol. 68, Issue 5, p443
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2024
  • ISSN:2398-7308
  • DOI:10.1093/annweh/wxae024
  • Accession Number:177947695
  • Copyright Statement:Copyright of Annals of Work Exposures & Health is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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