dc.contributor.author | Martínez-Zarzuela, Mario | |
dc.contributor.author | González Alonso, Javier | |
dc.contributor.author | Martín Tapia, Paula | |
dc.contributor.author | González Ortega, David | |
dc.contributor.author | Antón Rodríguez, Miriam | |
dc.contributor.author | Díaz Pernas, Francisco Javier | |
dc.date.accessioned | 2025-07-15T09:08:06Z | |
dc.date.available | 2025-07-15T09:08:06Z | |
dc.date.issued | 2025 | |
dc.identifier.citation | Expert Systems with Applications, 2025, vol. 278, p. 127212 | es |
dc.identifier.issn | 0957-4174 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/76348 | |
dc.description | Producción Científica | es |
dc.description.abstract | This study presents ME-WARD (Multimodal Ergonomic Workplace Assessment and Risk from Data), a novel system
for ergonomic assessment and musculoskeletal risk evaluation that implements the Rapid Upper Limb Assess-
ment (RULA) method. ME-WARD is designed to process joint angle data from motion capture systems, including
inertial measurement unit (IMU)-based setups, and deep learning human body pose tracking models. The tool’s
flexibility enables ergonomic risk assessment using any system capable of reliably measuring joint angles,
extending the applicability of RULA beyond proprietary setups. To validate its performance, the tool was tested
in an industrial setting during the assembly of conveyor belts, which involved high-risk tasks such as inserting
rods and pushing conveyor belt components. The experiments leveraged gold standard IMU systems alongside a
state-of-the-art monocular 3D pose estimation system. The results confirmed that ME-WARD produces reliable
RULA scores that closely align with IMU-derived metrics for flexion-dominated movements and comparable
performance with the monocular system, despite limitations in tracking lateral and rotational motions. This work
highlights the potential of integrating multiple motion capture technologies into a unified and accessible ergo-
nomic assessment pipeline. By supporting diverse input sources, including low-cost video-based systems, the
proposed multimodal approach offers a scalable, cost-effective solution for ergonomic assessments, paving the
way for broader adoption in resource-constrained industrial environments. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject.classification | Biomechanics | es |
dc.subject.classification | Ergonomics | es |
dc.subject.classification | IMU | es |
dc.subject.classification | Industry 4.0 | es |
dc.subject.classification | Computer vision | es |
dc.subject.classification | RULA | es |
dc.title | ME-WARD: A multimodal ergonomic analysis tool for musculoskeletal risk assessment from inertial and video data in working places | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2025 The Author(s) | es |
dc.identifier.doi | 10.1016/j.eswa.2025.127212 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417425008346 | es |
dc.identifier.publicationfirstpage | 127212 | es |
dc.identifier.publicationtitle | Expert Systems with Applications | es |
dc.identifier.publicationvolume | 278 | es |
dc.peerreviewed | SI | es |
dc.description.project | Ministerio de Ciencia e Innovación [PID2021-124515OA-I00] | es |
dc.description.project | Junta de Castilla y León - Consejería de Empleo e Industria de Castilla y León (under research project ErgoTwyn [INVESTUN/21/VA/0003]) | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 33 Ciencias Tecnológicas | es |