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AuthorEngelhardt, Lucasdc.contributor.author
Date of accession2021-11-03T14:33:11Zdc.date.accessioned
Available in OPARU since2021-11-03T14:33:11Zdc.date.available
Year of creation2021dc.date.created
Date of first publication2021-11-03dc.date.issued
AbstractMusculoskeletal 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.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
Has partEngelhardt, 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/biomechanics1010003dc.relation.haspart
Has partFonk, 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/s21041199dc.relation.haspart
Has partEngelhardt, 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/10.1080/10255842.2020.1851367dc.relation.haspart
LicenseLizenz Adc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/licenseA_v1dc.rights.uri
KeywordFracture Healing Simulationdc.subject
KeywordDistal Radiusdc.subject
KeywordHand Modeldc.subject
KeywordInverse Dynamicdc.subject
KeywordInverse Dynamicsdc.subject
KeywordHand Gripdc.subject
KeywordFracturedc.subject
KeywordBone Healingdc.subject
KeywordMuscledc.subject
KeywordPredictiondc.subject
KeywordMusculoskeletaldc.subject
KeywordComputational biologydc.subject
KeywordMechanotransductiondc.subject
KeywordMechanobiologydc.subject
Dewey Decimal GroupDDC 570 / Life sciencesdc.subject.ddc
Dewey Decimal GroupDDC 610 / Medicine & healthdc.subject.ddc
LCSHRemodellingdc.subject.lcsh
LCSHHealingdc.subject.lcsh
LCSHSimulationdc.subject.lcsh
LCSHModeldc.subject.lcsh
LCSHMusclesdc.subject.lcsh
LCSHForecastingdc.subject.lcsh
LCSHBiomechanicsdc.subject.lcsh
LCSHComputational biologydc.subject.lcsh
LCSHFinite element methoddc.subject.lcsh
MeSHBone and Bonesdc.subject.mesh
MeSHFracture Healingdc.subject.mesh
MeSHOsteogenesis, Distractiondc.subject.mesh
MeSHBiophysicsdc.subject.mesh
TitleFrom inverse dynamics hand modelling to distal radius fracture healing simulationdc.title
Resource typeDissertationdc.type
Date of acceptance2021-07-19dcterms.dateAccepted
RefereeUrban, Karstendc.contributor.referee
RefereeIgnatius, Anitadc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-39518dc.identifier.doi
PPN1776155513dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-39594-6dc.identifier.urn
GNDBruchdc.subject.gnd
GNDHeilungdc.subject.gnd
GNDModelldc.subject.gnd
GNDBewegungsapparatdc.subject.gnd
GNDBiomechanikdc.subject.gnd
GNDFinite-Elemente-Methodedc.subject.gnd
FacultyFakultät für Mathematik und Wirtschaftswissenschaftenuulm.affiliationGeneral
InstitutionInstitut für Numerische Mathematikuulm.affiliationSpecific
InstitutionUKU. Institut für Unfallchirurgische Forschung und Biomechanikuulm.affiliationSpecific
Grantor of degreeFakultät für Mathematik und Wirtschaftswissenschaftenuulm.thesisGrantor
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie
DFG project uulmMechanoregulation und Knochenumbau bei der Knochenheilung in gesunden, alten und osteoporotischen Patienten / DFG / 323231527uulm.projectDFG


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