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Comparison Between Observational Method, Wearable Inertial Measurement System and 4D Stereophotogrammetry for Ergonomics Risk Assessment: A Case Study
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy ; 4D4ALL Lab, Centre for Health and Technology, Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Belgium.ORCID iD: 0009-0002-7628-4049
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. 4D4ALL Lab, Centre for Health and Technology, Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, University of Antwerp, Belgium. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-0746-9816
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy.ORCID iD: 0000-0002-1020-6075
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-4596-3815
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2025 (English)In: Advances in Digital Human Modeling II: Proceedings of the 9th International Digital Human Modeling Symposium, DHM 2025, July 29-31, 2025, Loughborough, UK / [ed] Russell Marshall; Steve Summerskill; Gregor Harih; Sofia Scataglini, Cham: Springer, 2025, p. 193-206Conference paper, Published paper (Refereed)
Abstract [en]

Industry 5.0 places worker’s wellbeing at the center of the production process, prioritizing healthy and safety job conditions. Requirements to achieve occupational wellbeing are reducing risks for Work-related Musculoskeletal Disorders (WMSDs) and improving industry workstations. The traditional ergonomics risk assessments are based on human observational evaluation and the results are influenced by observers’ competence. Nowadays, advanced technologies such as motion capture systems are implemented to objectively monitor an operator’s movements over time. By providing real-time, data-driven insights into human movement and posture, systems offer the potential to reduce workplace injuries, enhance productivity, and promote long-term worker health. The purpose of the present study is to evaluate and compare three different approaches for assessing the quantitative biomechanical risk of an industrial task using the RULA method: the observational method, a wearable inertial measurement system, and a 4D stereophotogrammetry. The experiment involves one participant (female, 30 years old) performing a “pick-and-place” worker’s task in a controlled laboratory environment. RULA scores vary across the three approaches, with discrepancies primarily due to differences in how each system captures and measures joint angles. While this preliminary study provides valuable initial insights, the limitation of involving a single participant must be critically acknowledged. Future research will aim to include a larger sample size and conduct statistical analyses. The identification of benefits and limitations of each approach enables researchers, ergonomists, and industry stakeholders to critically select and integrate technology to support the worker’s safety, optimizing human wellbeing and overall system performance.

Place, publisher, year, edition, pages
Cham: Springer, 2025. p. 193-206
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 1577
National Category
Production Engineering, Human Work Science and Ergonomics Occupational Health and Environmental Health
Research subject
User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-25778DOI: 10.1007/978-3-032-00839-8_17ISI: 001594135400017ISBN: 978-3-032-00838-1 (print)ISBN: 978-3-032-00839-8 (electronic)OAI: oai:DiVA.org:his-25778DiVA, id: diva2:1993327
Conference
9th International Digital Human Modeling Symposium, DHM 2025, July 29-31, 2025, Loughborough, UK
Projects
LITMUS: Enabling the Transition from Industry 4.0 to Industry 5.0
Funder
Knowledge Foundation
Note

The study was conducted under the Erasmus + Traineeship and was supported by FWO medium-scale research infrastructure: 4D scanner or Accelerating Advanced motion Analysis and Application (I002020N), and in collaboration within the LITMUS project in Sweden, funded by The Knowledge Foundation and by the participating organizations.

Available from: 2025-08-29 Created: 2025-08-29 Last updated: 2026-05-21Bibliographically approved

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Perez Luque, EstelaHögberg, DanHanson, Lars

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Fontinovo, EugeniaPerez Luque, EstelaPapetti, AlessandraHögberg, DanHanson, LarsTruijen, StevenScataglini, Sofia
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Production Engineering, Human Work Science and ErgonomicsOccupational Health and Environmental Health

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