From inverse dynamics hand modelling to distal radius fracture healing simulation
Erstveröffentlichung
2021-11-03Autoren
Engelhardt, Lucas
Gutachter
Urban, KarstenIgnatius, Anita
Dissertation
Fakultäten
Fakultät für Mathematik und WirtschaftswissenschaftenInstitutionen
Institut für Numerische MathematikUKU. Institut für Unfallchirurgische Forschung und Biomechanik
Zusammenfassung
Musculoskeletal research questions regarding the prevention of chronic overloading or
rehabilitation of the hand can be addressed using inverse dynamics simulations when
experiments are not possible or ethically questionable. To date, no complete human
hand model implemented in a holistic human body model has been established. The
aim of the first part of this work was to develop, implement, and validate a detailed
hand model using the AnyBody Modelling System (AMS) (AnyBody, Aalborg, Denmark).
To achieve this, a consistent multiple cadaver dataset, including all extrinsic
and intrinsic muscles, served as a basis. Various obstacle methods were implemented
to obtain the correct alignment of the muscle paths. For model validation, experimental
datasets from the literature were used, which included the comparison of numerically
calculated moment arms of the wrist, thumb, and index finger muscles. In general,
the results displayed good comparability of the model and experimental data.
In contrast to this validation, the aim of the next study was to further validate the
hand model by analyzing numerically calculated muscle activities in comparison to
experimentally measured electromyographical signals of the muscles. Therefore, the
electromyographical signals of 10 hand muscles of five test subjects performing seven
different hand movements were measured. The kinematics of these tasks were used
as an input for the hand model, and the numerical muscle activities were computed.
To analyze the relationship between simulated and measured activities, the time
difference of the muscle on- and off-set points were calculated. The results showed
that the hand model fits the experiment quite accurately despite some limitations.
Therefore, this study is a further step towards patient-specific modelling of the upper
extremity.
This developed hand model was then applied to a specific hand gripping task, which
patients were urged to perform after a conservatively treated distal radius fracture.
Especially the transferred wrist load was of great interest, as it is the main mechanical
stimulus to the fractured radius healing performance. The musculoskeletal hand
model was adapted and used to determine muscle and joint forces in the wrist that
occur as a consequence of this hand grip contraction. The movement has been
captured precisely by motion capture analysis in combination with electromyographic
(EMG) data of all essential muscles in the forearm and hand. On top, the acting
contact forces have been determined with the aid of pressure mapping sensors attached
between the fingers and hand grip tool. By determining a characteristic force
distribution among the fingers, it was possible to apply the actual forces which act on
the individual fingers during the simulation. A comparison with in vivo studies showed
a good accordance of the predicted muscle force distribution and could determine a
prediction of the applied mechanical stimulus onto the fractured radius.
Simulating diaphyseal fracture healing via numerical models has been investigated for
a long time. It is apparent from in vivo studies that distal radius 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. Validation through
clinical data of distal radius fractures compared to corresponding geometrically patient-specific
fracture healing simulations was successful. Therefore, the model appeared
appropriate to study metaphyseal bone healing under differing mechanical conditions
and metaphyseal fractures in varying bones and fracture types.
Nevertheless, the model was conducted in a simplified rotational symmetric case.
Further studies identified crucial numerical problems in the algorithm to be able to
establish three dimensional simulations. We solved this by applying an iterative convolution
approach and decreased the computational demands with factors up to 70.
Preliminary results indicated, that the modelling approach, as depicted for rotational
symmetric geometries also showed plausible results in three dimensions. This model
and results will help optimizing clinical treatments on radial fractures, medical implant
design and foster biomechanical research in fracture healing.
Erstellung / Fertigstellung
2021
DFG-Projekt uulm
Mechanoregulation und Knochenumbau bei der Knochenheilung in gesunden, alten und osteoporotischen Patienten / DFG / 323231527
Kumulative Dissertation mit folgenden Artikeln
• Engelhardt, L.; Niemeyer, F.; Christen, P.; Müller, R.; Stock, K.; Blauth, M.; Urban, K.; Ignatius, A.; Simon, U. Simulating Metaphyseal Fracture Healing in the Distal Radius. Biomechanics 2021, 1, 29-42. https://doi.org/10.3390/biomechanics1010003
• Fonk, R.; Schneeweiss, S.; Simon, U.; Engelhardt, L. Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation. Sensors 2021, 21, 1199. https://doi.org/10.3390/s21041199
• Engelhardt, L.; Melzner, M.; Havelkova, L.; Fiala, P.; Christen, P.; Dendorfer, S.; Ulrich S. A New Musculoskeletal AnyBody Detailed Hand Model. Computer Methods in Biomechanics andBiomedical Engineering 2021, 24, 33-34. https://doi.org/10.1080/10255842.2020.1851367
• Fonk, R.; Schneeweiss, S.; Simon, U.; Engelhardt, L. Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation. Sensors 2021, 21, 1199. https://doi.org/10.3390/s21041199
• Engelhardt, L.; Melzner, M.; Havelkova, L.; Fiala, P.; Christen, P.; Dendorfer, S.; Ulrich S. A New Musculoskeletal AnyBody Detailed Hand Model. Computer Methods in Biomechanics andBiomedical Engineering 2021, 24, 33-34. https://doi.org/10.1080/10255842.2020.1851367
Schlagwörter
[GND]: Bruch | Heilung | Modell | Bewegungsapparat | Biomechanik | Finite-Elemente-Methode[LCSH]: Remodelling | Healing | Simulation | Model | Muscles | Forecasting | Biomechanics | Computational biology | Finite element method
[MeSH]: Bone and Bones | Fracture Healing | Osteogenesis, Distraction | Biophysics
[Freie Schlagwörter]: Fracture Healing Simulation | Distal Radius | Hand Model | Inverse Dynamic | Inverse Dynamics | Hand Grip | Fracture | Bone Healing | Muscle | Prediction | Musculoskeletal | Computational biology | Mechanotransduction | Mechanobiology
[DDC Sachgruppe]: DDC 570 / Life sciences | DDC 610 / Medicine & health
Metadata
Zur LanganzeigeDOI & Zitiervorlage
Nutzen Sie bitte diesen Identifier für Zitate & Links: http://dx.doi.org/10.18725/OPARU-39518
Engelhardt, Lucas (2021): From inverse dynamics hand modelling to distal radius fracture healing simulation. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. Dissertation. http://dx.doi.org/10.18725/OPARU-39518
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