[show abstract][hide abstract] ABSTRACT: The reigning paradigm of musculoskeletal modeling is to construct deterministic models from parameters of an "average" subject and make predictions for muscle forces and joint torques with this model. This approach is limited because it does not perform well for outliers, and it does not model the effects of population parameter variability. The purpose of this study was to simulate variability in musculoskeletal parameters on glenohumeral external rotation strength in healthy normals, and in rotator cuff tear case using a Monte Carlo model. The goal was to determine if variability in musculoskeletal parameters could quantifiably explain variability in glenohumeral external rotation strength. Multivariate Gamma distributions for musculoskeletal architecture and moment arm were constructed from empirical data. Gamma distributions of measured joint strength were constructed. Parameters were sampled from the distributions and input to the model to predict muscle forces and joint torques. The model predicted measured joint torques for healthy normals, subjects with supraspinatus tears, and subjects with infraspinatus-supraspinatus tears with small error. Muscle forces for the three conditions were predicted and compared. Variability in measured torques can be explained by differences in parameter variability.
Annals of Biomedical Engineering 04/2006; 34(3):465-76. · 2.58 Impact Factor
[show abstract][hide abstract] ABSTRACT: Shoulder strength data are important for post-operative assessment of shoulder function and have been used in diagnosis of rotator cuff pathology. Support vector machines (SVM) employ complex analysis techniques to solve classification and regression problems. A SVM, a machine learning technique, can be used for analysis and classification of shoulder strength data. The goals of this study were to determine the diagnostic competency of SVM based on shoulder strength data and to apply SVM analysis in efforts to derive a single representative shoulder strength score. Data were taken from fourteen isometric shoulder strength measurements of each shoulder (involved and uninvolved) in 45 rotator cuff tear patients. SVM diagnostic proficiency was found to be comparable to reported ultrasound values. Improvement of shoulder function was accurately represented by a single score in pairwise comparison of the pre-operative and the 12 month post-operative group (P < 0.004). Thus, the SVM-based score may be a promising metric for summarizing rotator cuff strength data.
Journal of Biomechanics 01/2006; 39(5):973-9. · 2.72 Impact Factor