bioRxivBioengineering
Video-driven simulation of lower limb mechanical loading during aquatic exercises
Abstract
Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating external resistance. Here I reconcile recent advances in 3D markerless pose and mesh estimation, biomechanical simulations, and hydrodynamic modeling, to predict lower limb mechanical loading during aquatic exercises. Simulations are driven exclusively from a single video. In silico hip and knee joint forces agreed well with in vivo instrumented implant recordings downloaded from the OrthoLoad database, both in magnitude and direction. New insights into individual muscle contributions to joint loading were gained. This noninvasive method has the potential to standardize the reporting of exercise intensity, inform the design of rehabilitation protocols and improve their reproducibility.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: biophysical modeling
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