Background: Knowledge of musculoskeletal parameters is essential to understanding and modeling a muscle's force generating capability. A study of musculoskeletal parameters was conducted in two parts: (I) Empirical measurement of upper extremity musculoskeletal parameters. (II) Computational bootstrap simulation to examine statistical power of detecting optimal muscle length as a function of sarcomere length sample size and effect size.
Methods: Parameters were determined with a cadaver model. Sarcomere lengths were measured for 120 samples per muscle using laser diffraction and the mean sarcomere length used to estimate optimal muscle length. A bootstrap computational simulation was conducted to estimate variance in mean sarcomere length as a function of sample size. Statistical power for detecting optimal muscle length as a function of sample size and effect size was then determined.
Findings: Parameters are reported in tabular format. Power is 80% at approximately 85, 50, 40 and 25 samples for effect sizes of 0.5, 0.75, 1.0 and 1.5 mm respectively.
Interpretation: Musculoskeletal parameters for predicting muscle forces can be adequately measured in a cadaver model. Measurement of 40-60 sarcomere lengths per muscle is sufficient to calculate mean sarcomere length for estimating optimal muscle length with power of 80% for an effect size of 0.75-1.0 mm.