Estimation of Shoulder Joint Reaction Forces and Moments Using MBS Dynamic Modeling

2014 ◽  
Vol 555 ◽  
pp. 701-706 ◽  
Author(s):  
Elena Mereuta ◽  
Daniel Ganea ◽  
Claudiu Mereuta
Keyword(s):  
Upper Limb ◽  
Shoulder Joint ◽  
Driving Force ◽  
Biceps Muscle ◽  
The Third ◽  
Flexion Extension ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Human Upper Limb ◽  
Joint Reaction

The paper presents a dynamic model created for estimating the magnitude of reaction forces and moments in the shoulder joint of the human upper limb. Considering that the flexion-extension motion of the forearm is simulated under three different conditions, the reaction forces and moments are determined. The first actuating case is corresponding to the case in which the driving force is acting on the long end of the biceps muscle. In the second case the driving force is acting on the short end of the biceps muscle, and in the third case the driving force is acting on both ends of the biceps muscle.

Prediction of Antagonistic Muscle Forces Using Inverse Dynamic Optimization During Flexion/Extension of the Knee

1999 ◽  
Vol 121 (3) ◽  
pp. 316-322 ◽  
Author(s):  
G. Li ◽  
K. R. Kaufman ◽  
E. Y. S. Chao ◽  
H. E. Rubash
Keyword(s):  
Dynamic Optimization ◽  
Optimization Procedure ◽  
Muscle Forces ◽  
Inverse Dynamic ◽  
Optimization Criteria ◽  
Flexion Extension ◽  
Antagonistic Muscle ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Joint Reaction

This paper examined the feasibility of using different optimization criteria in inverse dynamic optimization to predict antagonistic muscle forces and joint reaction forces during isokinetic flexion/extension and isometric extension exercises of the knee. Both quadriceps and hamstrings muscle groups were included in this study. The knee joint motion included flexion/extension, varus/valgus, and internal/external rotations. Four linear, nonlinear, and physiological optimization criteria were utilized in the optimization procedure. All optimization criteria adopted in this paper were shown to be able to predict antagonistic muscle contraction during flexion and extension of the knee. The predicted muscle forces were compared in temporal patterns with EMG activities (averaged data measured from five subjects). Joint reaction forces were predicted to be similar using all optimization criteria. In comparison with previous studies, these results suggested that the kinematic information involved in the inverse dynamic optimization plays an important role in prediction of the recruitment of antagonistic muscles rather than the selection of a particular optimization criterion. Therefore, it might be concluded that a properly formulated inverse dynamic optimization procedure should describe the knee joint rotation in three orthogonal planes.

Predicting Shoulder Joint Reaction Forces From 3D Body Kinematics: A Convolutional Neural Network Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 939-941
Author(s):  
Syed T. Mubarrat ◽  
Suman K. Chowdhury ◽  
Ashish D. Nimbarte
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shoulder Joint ◽  
Shoulder Injuries ◽  
Neural Network Approach ◽  
Good Convergence ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Occupational Settings ◽  
Joint Reaction

This study aimed to develop a convolutional neural network (CNN) model to predict shoulder joint reaction forces from 3D body kinematics. Results showed a good convergence between CNN model prediction and musculoskeletal model estimation for six novel tasks. Therefore, a CNN-based deep learning model can be used as a simple and relatively less time- and labor-intensive method to identify unsafe shoulder exertions in order to prevent the incidence of shoulder injuries or pathologies in occupational settings.

scholarly journals Manual material handling in the supermarket sector. Part 2: Knee, spine and shoulder joint reaction forces

2021 ◽  
Vol 92 ◽  
pp. 103345
Author(s):  
Sebastian Skals ◽  
Rúni Bláfoss ◽  
Lars Louis Andersen ◽  
Michael Skipper Andersen ◽  
Mark de Zee
Keyword(s):  
Shoulder Joint ◽  
Material Handling ◽  
Manual Material Handling ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Sector Part ◽  
Joint Reaction

Can shoulder joint reaction forces be estimated by neural networks?

2016 ◽  
Vol 49 (1) ◽  
pp. 73-79 ◽  
Author(s):  
W.H.K. de Vries ◽  
H.E.J. Veeger ◽  
C.T.M. Baten ◽  
F.C.T. van der Helm
Keyword(s):  
Neural Networks ◽  
Shoulder Joint ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Joint Reaction

Comparison of Three-Dimensional Patellofemoral Joint Reaction Forces in Persons With and Without Patellofemoral Pain

2014 ◽  
Vol 30 (4) ◽  
pp. 493-500 ◽  
Author(s):  
Yu-Jen Chen ◽  
Christopher M. Powers
Keyword(s):  
Patellofemoral Joint ◽  
Three Dimensional ◽  
Patellofemoral Pain ◽  
Biomechanical Analysis ◽  
Control Group ◽  
Three Dimensional Model ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Magnetic Resonance Imaging Mri ◽  
Joint Reaction

The purpose of this study was to determine if persons with patellofemoral pain (PFP) exhibit differences in patellofemoral joint reaction forces (PFJRFs) during functional activities. Forty females (20 PFP, 20 controls) underwent two phases of data collection: (1) magnetic resonance imaging (MRI) and (2) biomechanical analysis during walking, running, stair ascent, and stair descent. A previously described three-dimensional model was used to estimate PFJRFs. Resultant PFJRFs and the orthogonal components were reported. The PFP group demonstrated lower peak resultant PFJRFs and posterior component and superior component of the PFJRFs compared with the control group across all conditions. However, the PFP group had a higher peak lateral component of the PFJRF in three out of the four conditions evaluated. The lower resultant PFJRFs suggested that individuals with PFP may employ strategies to minimize patellofemoral joint loading, but it did not result in diminished lateral forces acting on the patella.

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