Subject-specific musculoskeletal modeling in the evaluation of shoulder muscle and joint function.
J Biomech. 2016 Nov 07;49(15):3626-3634
Authors: Wu W, Lee PV, Bryant AL, Galea M, Ackland DC
Upper limb muscle force estimation using Hill-type muscle models depends on musculotendon parameter values, which cannot be readily measured non-invasively. Generic and scaled-generic parameters may be quickly and easily employed, but these approaches do not account for an individual subject's joint torque capacity. The objective of the present study was to develop a subject-specific experimental testing and modeling framework to evaluate shoulder muscle and joint function during activities of daily living, and to assess the capacity of generic and scaled-generic musculotendon parameters to predict muscle and joint function. Three-dimensional musculoskeletal models of the shoulders of 6 healthy subjects were developed to calculate muscle and glenohumeral joint loading during abduction, flexion, horizontal flexion, nose touching and reaching using subject-specific, scaled-generic and generic musculotendon parameters. Muscle and glenohumeral joint forces calculated using generic and scaled-generic models were significantly different to those of subject-specific models (p<0.05), and task dependent; however, scaled-generic model calculations of shoulder glenohumeral joint force demonstrated better agreement with those of subject-specific models during abduction and flexion. Muscles in generic musculoskeletal models operated further from the plateau of their force-length curves than those of scaled-generic and subject-specific models, while muscles in subject-specific models operated over a wider region of their force length curves than those of the generic or scaled-generic models, reflecting diversity of subject shoulder strength. The findings of this study suggest that generic and scaled-generic musculotendon parameters may not provide sufficient accuracy in prediction of shoulder muscle and joint loading when compared to models that employ subject-specific parameter-estimation approaches.
PMID: 28327299 [PubMed - in process]