Robust estimation of lumbar joint forces in symmetric and asymmetric lifting tasks via large-scale...

Low back joint compression forces have been linked to the development of chronic back pain. Back-support exoskeletons controllers based on low back compression force estimates could potentially reduce the incidence of chronic pain. However, progress has been hampered by the lack of robust and accurate methods for compression force estimation. Electromyography (EMG)-driven musculoskeletal models have been proposed to estimate lumbar compression forces. Nonetheless, they commonly underrepresented trunk musculoskeletal geometries or activation-contraction dynamics, preventing validation across large sets of conditions. Here, we develop and validate a subject-specific large-scale (238 muscle-tendon units) EMG-driven musculoskeletal model for the estimation of lumbosacral moments and compression forces, under eight box-lifting conditions. Ten participants performed symmetric and asymmetric box liftings under 5 and 15 kg weight conditions. EMG-driven model-based estimates of L5/S1 flexion-extension moments displayed high correlation, R² (mean range: 0.88-0.94), and root mean squared errors between 0.21 and 0.38 Nm/kg, with respect to reference inverse dynamics moments. Model-derived muscle forces were utilized to compute lumbosacral compression forces, which reached eight times participants body weight in 15 kg liftings. For conditions involving stooped postures, model-based analyses revealed a predominant decrease in peak lumbar EMG amplitude during the lowering phase of liftings, which did not translate into a decrease in muscle-tendon forces. During eccentric contraction (box-lowering), our model employed the muscle force-velocity relationship to preserve muscle force despite significant EMG reduction. Our modeling methodology can inherently account for EMG-to-force non-linearities across subjects and lifting conditions, a crucial requirement for robust real-time control of back-support exoskeletons.