PROBABILISTIC BEHAVIOR OF JOINT FORCES IN MECHANISMS

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Boštjan Harl ◽  
Marko Kegl ◽  
Nenad Gubeljak ◽  
Dejan Dinevski
Keyword(s):  
Mining Industry ◽  
Hydraulic Support ◽  
Kinematic Chains ◽  
Taylor Approximation ◽  
The Monte Carlo Method ◽  
Joint Forces ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Joint Reaction ◽  
Actual Part

This paper discusses the influence of the clearance in joints on the joint reaction forces in mechanisms. By using mathematical programing, the optimal parameters of kinematic chains can be efficiently obtained by using the deterministic approach. However, the situation becomes more sophisticated if random effects of tolerances of the arm lengths and the random pin positions have to be considered. In this work the influence of clearances on joint forces is calculated by using the Taylor approximation and the Monte Carlo method. Using the two methods was necessary, because the Taylor approximation usually yields satisfying results only for small values of clearances and for this reason it makes sense to compute the required quantities also by another independent method. The implementation of the model is illustrated with two examples. The first example considers a closed loop chain, representing a four-bar mechanism being an actual part of a hydraulic support, employed in mining industry. The hydraulic support must fulfill some requirements so it’s very important to have influence of clearances on joint forces in control. The second example considers joint reaction forces of car wiper mechanism. It will be shown, that the clearance in joints have some influence on the joint reaction forces.

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.

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.

Influence of the Musculotendon Dynamics on the Muscle Force-Sharing Problem of the Shoulder—A Fully Inverse Dynamics Approach

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Quental Carlos ◽  
Azevedo Margarida ◽  
Ambrósio Jorge ◽  
Gonçalves S. B. ◽  
Folgado João
Keyword(s):  
Muscle Activation ◽  
Inverse Dynamics ◽  
Glenohumeral Joint ◽  
Inverse Dynamic ◽  
Force Sharing ◽  
Joint Reaction Forces ◽  
Reaction Forces ◽  
Muscle Activations ◽  
Joint Reaction

Abstract Most dynamic simulations are based on inverse dynamics, being the time-dependent physiological nature of the muscle properties rarely considered due to numerical challenges. Since the influence of muscle physiology on the consistency of inverse dynamics simulations remains unclear, the purpose of the present study is to evaluate the computational efficiency and biological validity of four musculotendon models that differ in the simulation of the muscle activation and contraction dynamics. Inverse dynamic analyses are performed using a spatial musculoskeletal model of the upper limb. The muscle force-sharing problem is solved for five repetitions of unloaded and loaded motions of shoulder abduction and shoulder flexion. The performance of the musculotendon models is evaluated by comparing muscle activation predictions with electromyography (EMG) signals, measured synchronously with motion for 11 muscles, and the glenohumeral joint reaction forces estimated numerically with those measured in vivo. The results show similar muscle activations for all muscle models. Overall, high cross-correlations are computed between muscle activations and the EMG signals measured for all movements analyzed, which provides confidence in the results. The glenohumeral joint reaction forces estimated compare well with those measured in vivo, but the influence of the muscle dynamics is found to be negligible. In conclusion, for slow-speed, standard movements of the upper limb, as those studied here, the activation and musculotendon contraction dynamics can be neglected in inverse dynamic analyses without compromising the prediction of muscle and joint reaction forces.

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