Refine
Year of publication
Document Type
- conference proceeding (article) (55)
- Article (50)
- conference proceeding (presentation, abstract) (11)
- conference talk (6)
- Part of a Book (2)
- Moving Images (1)
- Preprint (1)
Language
- English (126) (remove)
Is part of the Bibliography
- no (126) (remove)
Keywords
- Biomechanik (10)
- Simulation (9)
- Biomechanics (8)
- Biomechanische Analyse (7)
- Hüftgelenkprothese (7)
- AnyBody (4)
- Bewegungsapparat (4)
- Spongiosa (4)
- biomechanics (4)
- Inverse dynamics (3)
Institute
- Fakultät Maschinenbau (126)
- Labor Biomechanik (LBM) (125)
- Regensburg Center of Biomedical Engineering - RCBE (106)
- Regensburg Center of Health Sciences and Technology - RCHST (13)
- Labor Werkstoffprüfung und Metallographie (4)
- Labor Biofluidmechanik (2)
- Fakultät Angewandte Natur- und Kulturwissenschaften (1)
- Labor Faserverbundtechnik (LFT) (1)
- Labor Finite-Elemente-Methode (FEM) (1)
- Labor Medizinprodukte (1)
Begutachtungsstatus
- peer-reviewed (43)
- begutachtet (1)
The AnyBody™ Modeling System (AMS) [1], is an universally used musculoskeletal simulation software using inverse dynamics. Until now, no complete human hand model is known in the AMS. Also considering other musculoskeletal software platforms, just one detailed entire hand model is recently published [2] but is only based on one subject. The aim of this work is to implement a full detailed hand model for the AMS including all extrinsic and intrinsic muscles using data by the UWB gained through an anatomical study of ten cadaver hands.
Musculoskeletal modelling of the shoulder – effects on muscle recruitment and joint reaction force
(2019)
Thoracolumbar spine model with articulated rigcage for the prediction of dynamic spinal loading
(2015)
Navigated Femur First Total Hip Arthroplasty leads to improved Biomechanical Outcome after surgery
(2015)
Evaluation of muscle recruitment and muscle models in musculoskeletal simulation of dynamic motion
(2021)
Musculoskeletal simulation plays an increasingly important role in sports biomechanics. In the last years, the field of application widened from orthopaedics and ergonomics to sports [1]. A muscle recruitment algorithm with a quadratic objective function is usually used to calculate muscle activity in dynamic movements. The agreement of calculated and measured thigh muscle activity has already been investigated [2]. They found a strong agreement for sprinting and running, while the correlation decreased for side-cutting manoeuvres. Nevertheless, the influence of different muscle recruitment criteria on muscle activity in dynamic musculoskeletal simulations is currently unknown. Hence, this study aimed to analyse the effect of different muscle recruitment criteria and muscle models on the correlation of numerical and measured muscle activity in highly dynamic movements.