Biomechanical modelling in sports – selected applications

2008 ◽  
pp. 189-215
Author(s):  
U. Hartmann ◽  
G. Berti ◽  
J.G. Schmidt ◽  
T.M. Buzug
Keyword(s):  
Biomechanical Modelling

Biomechanical modelling of colon tissues

2006 ◽  
Vol 39 ◽  
pp. S648
Author(s):  
A.N. Natali ◽  
E.L. Carniel ◽  
P.G. Pavan ◽  
P. Dario ◽  
I. Izzo ◽  
...  
Keyword(s):  
Biomechanical Modelling

Biomechanical Modelling of the Brain for Neuronavigation in Epilepsy Surgery

2019 ◽  
pp. 165-180
Author(s):  
Karol Miller ◽  
Angus C. R. Tavner ◽  
Louis P. M. Menagé ◽  
Nicholas Psanoudakis ◽  
Grand Roman Joldes ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
Biomechanical Modelling ◽  
The Brain

Modeling the Human Elbow Joint Dynamics from Surface Electromyography

2014 ◽  
pp. 114-128 ◽  
Author(s):  
Andrés Felipe Ruiz-Olaya
Keyword(s):  
Postural Control ◽  
Surface Electromyography ◽  
Elbow Joint ◽  
Mechanical Impedance ◽  
External Perturbation ◽  
Human Movement ◽  
Biomechanical Modelling ◽  
Joint Dynamics ◽  
Human Motor ◽  
Human Joint

Biomechanical modelling and analysis of human motion are main topics of interest for a number of disciplines, ranging from biomechanics to human movement science. There exist various experimental and theoretical techniques developed to model the biomechanics and human motor system. A classic way to characterize a system is done by perturbation analysis, through applying an external perturbation and the observation of changes in the dynamic of system. In literature, human joint dynamics has been studied mainly in relation to external perturbations. However, those perturbations interact with the natural human motor behaviour. This chapter describes an approximation for non-invasive biomechanical modelling of the elbow joint dynamics from electromyographic information. A case study presents results obtained aimed at deriving a relationship between the dynamic behaviour of the human elbow joint and Surface Electromyography (SEMG) information in postural control. A set of experiments were carried out to measure bioelectrical (SEMG) and biomechanics information from human elbow joint, during postural control (i.e. isometric contractions) and correlate them with mechanical impedance at elbow joint. Estimates of elbow impedance were obtained by applying torque perturbations to the forearm. The results demonstrate that it is possible to estimate human joint dynamics from SEMG. The obtained results can contribute to the field of human motor control and also to its application in robotics and other engineering applications through the definition, specification and characterization of properties associated with the human upper limb and strategies used by people to command it.

Multiphase Liver Registration from Geodesic Distance Maps and Biomechanical Modelling

2013 ◽  
pp. 165-174 ◽  
Author(s):  
Jordan Bano ◽  
Stéphane A. Nicolau ◽  
Alexandre Hostettler ◽  
Christophe Doignon ◽  
Jacques Marescaux ◽  
...  
Keyword(s):  
Geodesic Distance ◽  
Biomechanical Modelling

Image-based biomechanical modelling in oral and maxillofacial practice

2006 ◽  
Vol 39 ◽  
pp. S427
Author(s):  
D. Trikeriotis ◽  
P. Diamantopoulos
Keyword(s):  
Biomechanical Modelling

scholarly journals Exoskeleton-based robotic platform applied in biomechanical modelling of the human upper limb

2009 ◽  
Vol 6 (2) ◽  
pp. 205-216 ◽  
Author(s):  
Andres F. Ruiz ◽  
Eduardo Rocon ◽  
Arturo Forner-Cordero
Keyword(s):  
Upper Limb ◽  
Robotic Platform ◽  
Biomechanical Modelling ◽  
Human Upper Limb

scholarly journals Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3690 ◽  
Author(s):  
Bernd J. Stetter ◽  
Steffen Ringhof ◽  
Frieder C. Krafft ◽  
Stefan Sell ◽  
Thorsten Stein
Keyword(s):  
Knee Joint ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Biomechanical Modelling ◽  
Joint Forces ◽  
Measurement Units ◽  
Artificial Neural Network Ann ◽  
The Right ◽  
Anterior Posterior ◽  
Good Agreement

Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications.

Three-dimensional surface geometries of the rabbit soleus muscle during contraction: input for biomechanical modelling and its validation

2013 ◽  
Vol 12 (6) ◽  
pp. 1205-1220 ◽  
Author(s):  
Markus Böl ◽  
Kay Leichsenring ◽  
Christine Weichert ◽  
Maike Sturmat ◽  
Philipp Schenk ◽  
...  
Keyword(s):  
Soleus Muscle ◽  
Three Dimensional ◽  
Biomechanical Modelling ◽  
Dimensional Surface
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