In musculoskeletal simulations muscle lengths and muscle force directions imply the characteristics of muscles acting around joints. Typically, the anatomical structure of the human body forces muscles to wrap around bones and neighboring tissue, thus most muscle paths cannot be represented adequately as straight lines. Therefore, biomechanical simulations require methods to compute musculotendon paths, their lengths, and their rates of length change to determine the muscle forces. This work focuses on a mechanical analogue to find the shortest path on general smooth surfaces, using a discrete variational principle. In this context, the geodesic path is reinterpreted as the constrained, force-free motion of a particle in n dimensions. The muscle path is then a G1-continuous combination of geodesics on adjacent obstacle surfaces [1,3,4].

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In light of the state-of-the-art treatment options for patients with rheumatoid arthritis (RA), a detailed and early quantification and detection of impaired hand function is desirable to allow personalized treatment regiments and amend currently used subjective patient reported outcome measures. This is the motivation to apply and adapt modern measurement technologies to quantify, assess and analyze human hand movement using a marker-based optoelectronic measurement system (OMS), which has been widely used to measure human motion. We complement these recordings with data from markerless (Doppler radar) sensors and data from both sensor technologies are integrated with clinical outcomes of hand function. The technologies are leveraged to identify hand movement characteristics in RA affected patients in comparison to healthy control subjects, while performing functional tests, such as the Moberg-Picking-Up Test. The results presented discuss the experimental framework and present the limiting factors imposed by the use of marker-based measurements on hand function. The comparison of simple finger motion data, collected by the OMS, to data recorded by a simple continuous wave radar suggests that radar is a promising option for the objective assessment of hand function. Overall, the broad scope of integrating two measurement technologies with traditional clinical tests shows promising potential for developing new pathways in understanding of the role of functional outcomes for the RA pathology.