Filtering of Multimodal Motion Capture Data through Individualized Musculoskeletal Human Models
Musculoskeletal simulation results are prone to various errors like measurement noise, soft tissue artifacts, and model inaccuracies. These errors lead to dynamically inconsistent simulation results which in turn diminish the meaningfulness of the results. For this reason, the sub-project C02 explores and investigates methods for filtering and analyzing multimodal motion measurement data (e.g. position, orientation, surface data, etc.) using individualized musculoskeletal models.
Through a systematic literature review, the importance of individualized models in musculoskeletal simulations was demonstrated. Generic musculoskeletal models will be individualized in multiple domains by scaling both the skeletal and muscular systems using manual measurement data and population-based data. To gather information about the strength percentile values of a person, EMG alignment based on MVC measurements will be used. Additionally, the investigation of the effect of joint axis individualization on computation results will be assessed.
The individualized musculoskeletal models will then be used to track multimodal experimental motion data, measured using the newly developed EmpkinS technology. A multimodal inverse kinematic method will be used to transfer the measurement data onto the individualized models. Afterwards, a dynamic tracking method should compensate measurement errors and generate dynamically consistent results. It will be researched, if such a sequential process is needed (kinematic tracking followed by dynamic tracking). Or if both kinematic data transfer and measurement error correction can be achieved using only a dynamic tracking method.
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