The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.
The developed CFPD model could be directly translated to the in vitro results. Hence, it can be applied for parameter screening and will contribute to the improvement of aerosol particle deposition at the olfactory cleft for CNS drug delivery in particular for biopharmaceuticals.
Simulating diaphyseal fracture healing via numerical models has been investigated for a long time. It is apparent from in vivo studies that metaphyseal fracture healing should follow similar biomechanical rules although the speed and healing pattern might differ. To investigate this hypothesis, a pre-existing, well-established diaphyseal fracture healing model was extended to study metaphyseal bone healing. Clinical data of distal radius fractures were compared to corresponding geometrically patient-specific fracture healing simulations. The numerical model, was able to predict a realistic fracture healing process in a wide variety of radius geometries. Endochondral and mainly intramembranous ossification was predicted in the fractured area without callus formation. The model, therefore, appears appropriate to study metaphyseal bone healing under differing mechanical conditions and metaphyseal fractures in different bones and fracture types. Nevertheless, the outlined model was conducted in a simplified rotational symmetric case. Further studies may extend the model to a three-dimensional representation to investigate complex fracture shapes. This will help to optimize clinical treatments of radial fractures, medical implant design and foster biomechanical research in metaphyseal fracture healing.
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