The prediction of muscular load sharing and joint forces in the lower extremities during walking

Seireg, A.; Arvikar, R.J.

doi:10.1016/0021-9290(75)90089-5

Cited by 445 publications

(167 citation statements)

References 15 publications

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“…All minimization principles failed to accurately predict antagonistic activity at the hip and knee Internal loading was found to depend on the description of joint kinematics. Muscle stresses squared combined with less constrained joints predicted synergistic and antagonistic muscle activities Provided information on the magnitudes and directions of pelvic muscle forces and acetabular contact forces during normal gait Seireg and Arvikar (1975) Crowninshield et al (1978) Patriarco et al…”

Section: Inverse Dynamics-based Static Optimizationmentioning

confidence: 99%

Model-based estimation of muscle forces exerted during movements

Erdemir

McLean

Herzog

et al. 2007

Clinical Biomechanics

732

543

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Section: Inverse Dynamics-based Static Optimizationmentioning

confidence: 99%

Model-based estimation of muscle forces exerted during movements

Erdemir

McLean

Herzog

et al. 2007

Clinical Biomechanics

732

543

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“…Modeling is complicated by the high degree of mechanical redundancy attributable to the numerous muscles involved in knee motion, some of which are biarticular. As a result, theoretic estimates of tibiofemoral forces have been inconsistent and vary widely based on the mathematical model used and the activity analyzed [6,31,33,47,58]. For example, a two-dimensional static analysis of maximum voluntary isokinetic knee extension with a single quadriceps muscle estimated peak tibiofemoral compressive forces up to 9 times body weight (9BW) [43], whereas a twodimensional inverse dynamics model using a linear optimization algorithm to solve for the distribution of synergistic muscle forces estimated peak compressive forces of only 4 9 BW during maximal voluntary effort [23].…”

Section: Introductionmentioning

confidence: 99%

“…For example, a two-dimensional static analysis of maximum voluntary isokinetic knee extension with a single quadriceps muscle estimated peak tibiofemoral compressive forces up to 9 times body weight (9BW) [43], whereas a twodimensional inverse dynamics model using a linear optimization algorithm to solve for the distribution of synergistic muscle forces estimated peak compressive forces of only 4 9 BW during maximal voluntary effort [23]. Knee forces predicted for walking range from 1.7 9 BW to 7 9 BW [6,27,28,47].…”

Section: Introductionmentioning

confidence: 99%

The 2011 ABJS Nicolas Andry Award: ‘Lab’-in-a-Knee: In Vivo Knee Forces, Kinematics, and Contact Analysis

D’Lima

Patil

Steklov

et al. 2011

Clinical Orthopaedics &Amp; Related Research

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Background Tibiofemoral forces are important in the design and clinical outcomes of TKA. We developed a tibial tray with force transducers and a telemetry system to directly measure tibiofemoral compressive forces in vivo. Knee forces and kinematics traditionally have been measured under laboratory conditions. Although this approach is useful for quantitative measurements and experimental studies, the extrapolation of results to clinical conditions may not always be valid. Questions/purposes We therefore developed wearable monitoring equipment and computer algorithms for classifying and identifying unsupervised activities outside the laboratory. Methods Tibial forces were measured for activities of daily living, athletic and recreational activities, and with orthotics and braces, during 4 years postoperatively. Additional measurements included video motion analysis, EMG, fluoroscopic kinematic analysis, and ground reaction force measurement. In vivo measurements were used to evaluate computer models of the knee. Finite element models were used for contact analysis and for computing knee kinematics from measured knee forces. A thirdgeneration system was developed for continuous monitoring of knee forces and kinematics outside the laboratory using a wearable data acquisition hardware. Results By using measured knee forces and knee flexion angle, we were able to compute femorotibial AP translation (À12 to +4 mm), mediolateral translation (À1 to 1.5 mm), axial rotation (À3°to 12°), and adduction-abduction (À1°t o +1°). The neural-network-based classification system was able to identify walking, stair-climbing, sit-to-stand, and stand-to-sit activities with 100% accuracy. Conclusions Our data may be used to improve existing in vitro models and wear simulators, and enhance prosthetic designs and biomaterials.

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“…, 8). Due to the control overactuation in musculoskeletal systems, the (redundant) control problem is usually solved using optimization techniques [1][2][3][4][5][6][7][35][36][37][38] that apply some predetermined criteria to share the muscular joint torques from the inverse dynamics analysis into the individual muscle efforts. Most often, the redundancy of muscular load sharing is addressed by minimizing a cost (or objective) function appropriately selected for the movement under investigation.…”

Section: Muscle Force Estimationmentioning

confidence: 99%

Influence of selected modeling and computational issues on muscle force estimates

Blajer

Czaplicki²,

Dziewiecki

et al. 2010

Multibody Syst Dyn

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Knowledge of muscle forces and joint reaction forces during human movement can provide insight into the underlying control and tissue loading. Since direct measurement of the internal loads is generally not feasible, non-invasive methods based on musculoskeletal modeling and computer simulations have been extensively developed. By applying observed motion data to the musculoskeletal models, inverse dynamic analysis allow to determine the resultant joint torques, transformed then into estimates of individual muscle forces by means of different optimization procedures. Assessment of the joint reaction forces and other internal loads is further possible. Comparison of the muscle force estimates obtained for different modeling assumptions and parameters in the model can be valuable for the improvement of validity of the model-based estimations. The present study is another contribution to this field. Using a sagittal plane model of an upper limb with a weight carried in hand, and applying the data of recorded flexion and extension movement of the upper limb, the resultant muscular forces are predicted using different modeling assumptions and simulation tools. This study relates to different coordinates (joint and natural coordinates) used to built the mathematical model, muscle path modeling, muscle decomposition (change in number of the modeled muscles), and different optimization methods used to share the joint torques into individual muscles.

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The prediction of muscular load sharing and joint forces in the lower extremities during walking

Cited by 445 publications

References 15 publications

Model-based estimation of muscle forces exerted during movements

Model-based estimation of muscle forces exerted during movements

The 2011 ABJS Nicolas Andry Award: ‘Lab’-in-a-Knee: In Vivo Knee Forces, Kinematics, and Contact Analysis

Influence of selected modeling and computational issues on muscle force estimates

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