Musculoskeletal Model of the Human Knee With Representation of Menisci During the Stance Phase of a Walk Cycle

Author(s):  
Mohammad Kia ◽  
Trent M. Guess ◽  
Antonis Stylianou

Movement simulation and musculoskeletal modeling can predict muscle forces, but current methods are hindered by simplified representations of joint structures. Simulations that incorporate muscle forces, an anatomical representation of the natural knee, and contact mechanics would be a powerful tool in orthopedics. This study combined a validated anatomical model of a knee joint with menisci and a musculoskeletal model of the human lower extremity. A forward-dynamics muscle driven simulation of the stance phase of a walk cycle was simulated in LifeMOD (Lifemodeler, Inc) and muscle forces and ground reaction forces were estimated. The predicted forces were evaluated using test data provided by Vaughan CL. et al. (1999).

2021 ◽  
Vol 11 (5) ◽  
pp. 2356
Author(s):  
Carlo Albino Frigo ◽  
Lucia Donno

A musculoskeletal model was developed to analyze the tensions of the knee joint ligaments during walking and to understand how they change with changes in the muscle forces. The model included the femur, tibia, patella and all components of cruciate and collateral ligaments, quadriceps, hamstrings and gastrocnemius muscles. Inputs to the model were the muscle forces, estimated by a static optimization approach, the external loads (ground reaction forces and moments) and the knee flexion/extension movement corresponding to natural walking. The remaining rotational and translational movements were obtained as a result of the dynamic equilibrium of forces. The validation of the model was done by comparing our results with literature data. Several simulations were carried out by sequentially removing the forces of the different muscle groups. Deactivation of the quadriceps produced a decrease of tension in the anterior cruciate ligament (ACL) and an increase in the posterior cruciate ligament (PCL). By removing the hamstrings, the tension of ACL increased at the late swing phase, while the PCL force dropped to zero. Specific effects were observed also at the medial and lateral collateral ligaments. The removal of gastrocnemius muscles produced an increase of tension only on PCL and lateral collateral ligaments. These results demonstrate how musculoskeletal models can contribute to knowledge about complex biomechanical systems as the knee joint.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7709
Author(s):  
Serena Cerfoglio ◽  
Manuela Galli ◽  
Marco Tarabini ◽  
Filippo Bertozzi ◽  
Chiarella Sforza ◽  
...  

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.


2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Lyle T. Jackson ◽  
Patrick M. Aubin ◽  
Matthew S. Cowley ◽  
Bruce J. Sangeorzan ◽  
William R. Ledoux

The symptomatic flatfoot deformity (pes planus with peri-talar subluxation) can be a debilitating condition. Cadaveric flatfoot models have been employed to study the etiology of the deformity, as well as invasive and noninvasive surgical treatment strategies, by evaluating bone positions. Prior cadaveric flatfoot simulators, however, have not leveraged industrial robotic technologies, which provide several advantages as compared with the previously developed custom fabricated devices. Utilizing a robotic device allows the researcher to experimentally evaluate the flatfoot model at many static instants in the gait cycle, compared with most studies, which model only one to a maximum of three instances. Furthermore, the cadaveric tibia can be statically positioned with more degrees of freedom and with a greater accuracy, and then a custom device typically allows. We created a six degree of freedom robotic cadaveric simulator and used it with a flatfoot model to quantify static bone positions at ten discrete instants over the stance phase of gait. In vivo tibial gait kinematics and ground reaction forces were averaged from ten flatfoot subjects. A fresh frozen cadaveric lower limb was dissected and mounted in the robotic gait simulator (RGS). Biomechanically realistic extrinsic tendon forces, tibial kinematics, and vertical ground reaction forces were applied to the limb. In vitro bone angular position of the tibia, calcaneus, talus, navicular, medial cuneiform, and first metatarsal were recorded between 0% and 90% of stance phase at discrete 10% increments using a retroreflective six-camera motion analysis system. The foot was conditioned flat through ligament attenuation and axial cyclic loading. Post-flat testing was repeated to study the pes planus deformity. Comparison was then made between the pre-flat and post-flat conditions. The RGS was able to recreate ten gait positions of the in vivo pes planus subjects in static increments. The in vitro vertical ground reaction force was within ±1 standard deviation (SD) of the in vivo data. The in vitro sagittal, coronal, and transverse plane tibial kinematics were almost entirely within ±1 SD of the in vivo data. The model showed changes consistent with the flexible flatfoot pathology including the collapse of the medial arch and abduction of the forefoot, despite unexpected hindfoot inversion. Unlike previous static flatfoot models that use simplified tibial degrees of freedom to characterize only the midpoint of the stance phase or at most three gait positions, our simulator represented the stance phase of gait with ten discrete positions and with six tibial degrees of freedom. This system has the potential to replicate foot function to permit both noninvasive and surgical treatment evaluations throughout the stance phase of gait, perhaps eliciting unknown advantages or disadvantages of these treatments at other points in the gait cycle.


Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 136
Author(s):  
Ophelie Lariviere ◽  
Thomas Provot ◽  
Laura Valdes-Tamayo ◽  
Maxime Bourgain ◽  
Delphine Chadefaux

Although accelerometers’ responses during running are not perfectly understood, they are widely used to study performance and the risk of injury. To outline the typical tibial acceleration pattern during running, this study aims to investigate the repeatability of acceleration signals with respect to the ground reaction force waveforms. Ten amateur runners were asked to perform ten trials along a straight line. One participant was asked to perform this protocol over ten sessions. Tibial accelerations and ground reaction forces were measured during the stance phase. The coefficient of multiple correlation R was computed to study the intra- and inter-test and subject repeatability of accelerometric and force waveforms. A good (R>0.8) intra- and inter-test repeatability was observed for all measured signals. Similar results were observed for intra-subject repeatability. A good inter-subject repeatability was observed only for the longitudinal acceleration and vertical and antero-posterior forces. Typical accelerometric signatures were outlined for each case studied.


2003 ◽  
Vol 358 (1437) ◽  
pp. 1493-1500 ◽  
Author(s):  
E. Otten

Connected multi–body systems exhibit notoriously complex behaviour when driven by external and internal forces and torques. The problem of reconstructing the internal forces and/or torques from the movements and known external forces is called the ‘inverse dynamics problem’, whereas calculating motion from known internal forces and/or torques and resulting reaction forces is called the ‘forward dynamics problem’. When stepping forward to cross the street, people use muscle forces that generate angular accelerations of their body segments and, by virtue of reaction forces from the street, a forward acceleration of the centre of mass of their body. Inverse dynamics calculations applied to a set of motion data from such an event can teach us how temporal patterns of joint torques were responsible for the observed motion. In forward dynamics calculations we may attempt to create motion from such temporal patterns, which is extremely difficult, because of the complex mechanical linkage along the chains forming the multi–body system. To understand, predict and sometimes control multi–body systems, we may want to have mathematical expressions for them. The Newton–Euler, Lagrangian and Featherstone approaches have their advantages and disadvantages. The simulation of collisions and the inclusion of muscle forces or other internal forces are discussed. Also, the possibility to perform a mixed inverse and forward dynamics calculation are dealt with. The use and limitations of these approaches form the conclusion.


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