Lim, Sol2025-04-282025-04-282024-11-010014-0139https://hdl.handle.net/10919/126235Wearable inertial measurement units (IMUs) are used increasingly to estimate biomechanical exposures in lifting-lowering tasks. The objective of the study was to develop and evaluate predictive models for estimating relative hand loads and two other critical biomechanical exposures to gain a comprehensive understanding of work-related musculoskeletal disorders in lifting. We collected 12,480 lifting-lowering phases from 26 subjects (15 men and 11 women) performing manual lifting-lowering tasks with hand loads (0–22.7 kg) at varied workstation heights and handling modes. We implemented a Hierarchical model, that sequentially classified risk factors, including workstation height, handling mode, and relative hand load. Our algorithm detected lifting-lowering phases (>97.8%) with mean onset errors of 0.12 and 0.2 seconds for lifting and lowering phases. It estimated workstation height (>98.5%), handling mode (>87.1%), and relative hand load (mean absolute errors of 5.6–5.8%) across conditions, highlighting the benefits of data-driven models in deriving lifting-lowering occurrences, timing, and critical risk factors from continuous IMU-based kinematics.Pages 1596-161116 page(s)application/pdfenIn CopyrightInertial sensor measurementslifting-loweringrisk factors estimationbiomechanical exposuresdata-driven algorithmsHandHumansMusculoskeletal DiseasesOccupational DiseasesRisk FactorsTask Performance and AnalysisLiftingWeight-BearingAdultFemaleMaleYoung AdultAccelerometryBiomechanical PhenomenaWearable Electronic DevicesArticle - Refereedhttps://doi.org/10.1080/00140139.2024.23439496711Lim, Sol [0000-0001-5569-9312]386468711366-5847