RT info:eu-repo/semantics/article
T1 ME-WARD: A multimodal ergonomic analysis tool for musculoskeletal risk assessment from inertial and video data in working places
A1 Martínez-Zarzuela, Mario
A1 González Alonso, Javier
A1 Martín Tapia, Paula
A1 González Ortega, David
A1 Antón Rodríguez, Miriam
A1 Díaz Pernas, Francisco Javier
K1 Biomechanics
K1 Ergonomics
K1 IMU
K1 Industry 4.0
K1 Computer vision
K1 RULA
K1 33 Ciencias Tecnológicas
AB This study presents ME-WARD (Multimodal Ergonomic Workplace Assessment and Risk from Data), a novel systemfor 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, includinginertial measurement unit (IMU)-based setups, and deep learning human body pose tracking models. The tool’sflexibility 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 testedin an industrial setting during the assembly of conveyor belts, which involved high-risk tasks such as insertingrods and pushing conveyor belt components. The experiments leveraged gold standard IMU systems alongside astate-of-the-art monocular 3D pose estimation system. The results confirmed that ME-WARD produces reliableRULA scores that closely align with IMU-derived metrics for flexion-dominated movements and comparableperformance with the monocular system, despite limitations in tracking lateral and rotational motions. This workhighlights 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, theproposed multimodal approach offers a scalable, cost-effective solution for ergonomic assessments, paving theway for broader adoption in resource-constrained industrial environments.
PB Elsevier
SN 0957-4174
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/76348
UL https://uvadoc.uva.es/handle/10324/76348
LA eng
NO Expert Systems with Applications, 2025, vol. 278, p. 127212
NO Producción Científica
DS UVaDOC
RD 19-jul-2025