To reduce the incidence of occupational musculoskeletal disorders, back-support exoskeletons are being introduced to assist manual material handling activities. Using a device of this type, this study investigates the effects of a new control strategy that uses the angular acceleration of the user’s trunk to assist during lifting tasks. To validate this new strategy, its effectiveness was experimentally evaluated relative to the condition without the exoskeleton as well as against existing strategies for comparison. Using the exoskeleton during lifting tasks reduced the peak compression force on the L5S1 disc by up to 16%, with all the control strategies. Substantial differences between the control strategies in the reductions of compression force, lumbar moment and back muscle activation were not observed. However, the new control strategy reduced the movement speed less with respect to the existing strategies. Thanks to improved timing in the assistance in relation to the typical dynamics of the target task, the hindrance to typical movements appeared reduced, thereby promoting intuitiveness and comfort.

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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 EMGdriven 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.