Musculoskeletal models, like all theoretical models of physical processes, depend on the assumptions needed to construct the model. For musculoskeletal models, these assumptions include, among other things, the kinematic data, the kinetic data and the muscle parameters. The former (dynamic) data can be acquired relatively easily from living subjects, but the latter are usually based on limited information, frequently determined from cadaver studies performed on elderly individuals. Previously, we determined the sensitivity of forces to dynamic differences among 10 humans walking on a straight path. Here, we assess the sensitivity of the muscle and joint reaction forces developed in human walking to variable muscle parameters obtained from 10 living adults, whose data were recently reported, and compared the results with the values from a standard model that depends on cadaveric data. We found that, while the force patterns across the stance cycle were similar among muscle parameter models, differences of as much as 15% in the force magnitude were produced. Whether or not the variation between the standard model and other muscle parameters is important depends on why the forces are required.

This study investigates the accuracy of an automatic image analysis method that was developed for spiral computed tomography scans (SCTS), with the objective of calculating the volume of muscle in the hind leg (HLMVCT) and lumbar region (LRMVCT) in lambs. The first step in the image analysis method was the isolation (segmentation) of the muscle regions in each image of the SCTS, using a new program that was implemented in the Sheep Tomogram Analysis Routines software (STAR). Due to the differences of muscle shape in the regions investigated, the new segmentation program applies different segmentation paths in specific subregions. These were automatically identified by the program based on skeletal landmarks. After the segmentation was completed, the muscles areas were automatically measured by counting the pixels representing muscle in each image; the volumes were calculated by adding the muscle areas of each image multiplied by the depth of the image (inter-slice distance). The accuracy of these measures of muscle volume was evaluated, using regression analysis, by comparing HLMVCT and LRMVCT to the hind leg and lumbar region muscle weights measured after dissection (HLMWD, no. =240, and LRMWD, no. =50, respectively) of Texel (TEX) and Scottish Blackface (SBF) female and male lambs slaughtered in 2003-04. The effects of breed, sex and year on the association (SCTS v. dissection) were evaluated. There was a strong association between HLMVCT and HLMWD ( R2=97·4%), which only increased slightly ( R2=97·7%) when breed was included in the model. This indicates that HLMWD can be estimated directly from HLMVCT with a high degree of accuracy. For the lumbar region, the association was high ( R2=83·0% to 88·8% depending on the model) but lower than in the hind leg, probably because the automatic segmentation isolates only the areas of the longissimus lumborum and multifidi muscles. Breed had a significant effect on the prediction of LRMWD from LRMVCT, as well as sex in the case of the TEX lambs. The results indicated that the predictions of LRMWD from LRMVCT require different equations for very divergent breeds such as TEX and SBF.