INTRODUCTION: Musculoskeletal modeling offers a unique possibility to predict the internal loading acting on joint-muscle structures during sport-specific movements in winter sports, e.g. cross-country skiing and alpine skiing 1-4. To accurately represent any individual, a musculoskeletal model`s anthropometry and muscle strength must be scaled. Parameters that govern muscle strength are highly subject specific which affects the accuracy of predicted internal loading. The standard anthropometric scaling method (LMS) may be sufficient for the general population, but better accuracy is required for elite athletes due to large muscle structure deviations. We propose a method for scaling subject-specific muscle parameters. METHODS: Participants with prior experience in heavy resistance training were included in the study (n = 3, mean height = 1.82 m, mean weight = 82.7 kg). They performed isometric knee extension MVC`s at six knee angles. Torque and knee angle were measured and used to drive a musculoskeletal model. Models were scaled using only LMS or with added muscle strength and fiber length optimization (SLQP) 5,6. The isometric quadriceps strength calculated by each model was compared to the experimental data (fig. 1). RESULTS: LMS scaling produced the largest deviations with an average error of 46%, while the average error of SLQP only deviated 9%. Figure 1, a comparison between LMS and SLQP indicates the results as a percentage of deviation from measured torques. > 0 overestimation of torque, < 0 underestimation of torque. DISCUSSION/CONCLUSION: The results imply that SLQP scaling improves model predictions that rely on maximal muscular strength and may increase the accuracy and validity of musculoskeletal models used on athletic populations. There is a need for further research, including SLQP scaling on winter sports athletes, which is planned in the continuation of the current project.
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