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

Author(s):  
Syed T. Mubarrat ◽  
Suman K. Chowdhury ◽  
Ashish D. Nimbarte

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.

2014 ◽  
Vol 555 ◽  
pp. 701-706 ◽  
Author(s):  
Elena Mereuta ◽  
Daniel Ganea ◽  
Claudiu Mereuta

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.


2021 ◽  
Vol 92 ◽  
pp. 103345
Author(s):  
Sebastian Skals ◽  
Rúni Bláfoss ◽  
Lars Louis Andersen ◽  
Michael Skipper Andersen ◽  
Mark de Zee

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

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