Wearable 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.
Keywords: Inertial sensor measurements; biomechanical exposures; data-driven algorithms; lifting-lowering; risk factors estimation.
The study developed and validated algorithms for detecting and predicting exposure to various risk factors during diverse lifting-lowering tasks. These factors encompass the occurrence, timing, workstation height, handling mode, and relative hand position. This approach facilitates the extraction of contextual information related to lifting tasks conducted in real-world settings through a continuous stream of inertial sensor measurements. Consequently, it can enable automated risk assessment for lifting activities in the field.