Conference Agenda

Work-related musculoskeletal disorders remain widespread and costly, while traditional ergonomic assessment methods are limited by subjectivity, time demands, and sparse temporal sampling. Inertial Measurement Unit (IMU)-based motion capture offers a scalable solution for continuous monitoring, although professional systems are often too expensive for widespread use. This study investigates the suitability of an affordable IMU system, Perception Neuron 3 (PN), for objective ergonomic risk assessment. A dedicated algorithmic pipeline was developed to extract upper-limb kinematics and automatically compute OCRA indices and the Revised NIOSH Lifting Equation. The evaluation followed a two-step approach: laboratory tests compared PN with a professional reference system (Xsens MVN Awinda), while industrial tests benchmarked algorithm-derived outputs against expert ergonomic assessments. Results show a high correlation between PN and Xsens for joint angles and NIOSH geometric parameters, with discrepancies remaining within acceptable ranges for ergonomic applications. On-field comparisons reveal systematic differences between automated and observational assessments, mainly due to continuous kinematic measurement versus visual estimation, as well as interobserver variability in manual procedures. Overall, the findings support the feasibility of using affordable IMU systems for automated ergonomic evaluation in industrial environments, while highlighting the need for further refinement and validation for full integration into ergonomic assessment workflows.