Muscle-level analysis of trunk mechanics via musculoskeletal modeling and high-density electromyograms

Back-support (BS) exoskeletons aim at preventing or minimizing low-back pain in workers within occupational environments. Currently, there is no consensus on the optimal controller for BS exoskeletons. We propose a controller based on electromyography (EMG)-informed musculoskeletal modeling that estimates back muscle-tendon forces and moments. In this study, we validate an EMG-driven trunk model to estimate flexion-extension moments at the lumbar L5/S1 joint, during symmetric lifting tasks. In a first experimental session, ground reaction forces, subject kinematics and bipolar EMG activity from abdominal and lumbar muscles were recorded to estimate L5/S1 moments using both, inverse dynamics (ID) and EMG-driven modeling approaches. One subject performed squatting and stooping lifting tasks with three weight conditions (0, 5 and 15 kg). Correlation coefficients, R
²
, between reference moments (from ID) and corresponding EMG-driven estimates ranged between 0.94 and 0.98, with root mean squared errors between 10.23 and 20.30 Nm. In a second experimental session,}4 high-density EMG (HDEMG) grids (256 channels) were used to generate high-fidelity topographical activation maps of thoracolumbar muscles during lifting tasks. These maps revealed that lifting objects using the squatting technique, underlay a shift of activation from caudal muscle trunk regions to cranial areas while lowering the weights. Muscle forces derived from EMG-driven modeling altogether with HDEMG activation maps are here proposed as a new framework to understand trunk neuromechanics during complex lifting tasks.