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L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
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L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment

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L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment
Journal Article

L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment

2025
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Overview
Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This article proposes the parametric modeling of an active lumbar exoskeleton using the Forces ergonomic method, which calculates the ergonomic risk using motion capture in the workplace, considering the internal joint forces. The exoskeleton was studied to model it in the Forces method using a four-phase approach based on experimental observations (Phase 1) and objective data collection via motion capture with inertial sensors and load cells for lifting load movements. From the experimentation the angles of each body segment, the effort perceived by the user, and the activation conditions were obtained (Phase 2). After modeling development (Phase 3), the experimental results regarding the force and risk were evaluated obtaining differences between model and experimental data of 0.971 ± 0.171 kg in chest force and 1.983 ± 0.678% in lumbar risk (Phase 4). This approach provides a tool to evaluate the biomechanical effects of this device in a work task, offering a parametric and direct approximation of the effects prior to implementation.