Direct comparison of calculated hip joint contact forces with those measured using instrumented implants. An evaluation of a three-dimensional mathematical model of the lower limb

Stansfield, Ben; Nicol, A.C.; Paul, J. P.; Kelly, I.G.; Graichen, F.; Bergmann, G.

doi:10.1016/s0021-9290(03)00072-1

Cited by 141 publications

(133 citation statements)

References 15 publications

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“…[3][4][5][6][7][8] Muscle forces calculated by a musculoskeletal model predicted hip contact forces during walking in close agreement with measurements made by a force-measuring hip implant. 5 When the same model was applied to the knee, however, predicted contact forces during gait were much higher than previously reported knee contact forces. 7 Some researchers also attempted to validate model predictions of kneejoint loading against measurements obtained from instrumented joint replacements implanted into cadaver specimens.…”

supporting

confidence: 72%

Evaluation of predicted knee‐joint muscle forces during gait using an instrumented knee implant

Kim

Fernandez

Akbarshahi

et al. 2009

Journal Orthopaedic Research

160

110

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Musculoskeletal modeling and optimization theory are often used to determine muscle forces in vivo. However, convincing quantitative evaluation of these predictions has been limited to date. The present study evaluated model predictions of knee muscle forces during walking using in vivo measurements of joint contact loading acquired from an instrumented implant. Joint motion, ground reaction force, and tibial contact force data were recorded simultaneously from a single subject walking at slow, normal, and fast speeds. The body was modeled as an 8-segment, 21-degree-of-freedom articulated linkage, actuated by 58 muscles. Joint moments obtained from inverse dynamics were decomposed into leg-muscle forces by solving an optimization problem that minimized the sum of the squares of the muscle activations. The predicted knee muscle forces were input into a 3D knee implant contact model to calculate tibial contact forces. Calculated and measured tibial contact forces were in good agreement for all three walking speeds. The average RMS errors for the medial, lateral, and total contact forces over the entire gait cycle and across all trials were 140 AE 40 N, 115 AE 32 N, and 183 AE 45 N, respectively. Muscle coordination predicted by the model was also consistent with EMG measurements reported for normal walking. The combined experimental and modeling approach used in this study provides a quantitative framework for evaluating model predictions of muscle forces in human movement. ß

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supporting

confidence: 72%

Evaluation of predicted knee‐joint muscle forces during gait using an instrumented knee implant

Kim

Fernandez

Akbarshahi

et al. 2009

Journal Orthopaedic Research

160

110

View full text Add to dashboard Cite

show abstract

“…Also, within the static approaches, there are differences according to the choice of the optimization criterion [18]. However, comparison of measurements and calculations of the hip joint reaction force showed that the type of the optimization criterion employed does not significantly influence the calculated hip joint reaction force [25].…”

Section: Discussionmentioning

confidence: 99%

Acetabular Loading in Active Abduction

Kristan

Mavčič

Cimerman

et al. 2007

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Operative fixation of fragments in acetabular fracture treatment is not strong enough to allow weight bearing before the bone is healed. In some patients, even passive or active nonweight-bearing exercises could lead to dislocation of fragments and posttraumatic osteoarthritis. Therefore, early rehabilitation should avoid loading the acetabulum in the regions of fracture lines. The aim of the paper is to estimate acetabular loading in nonweight-bearing upright, supine, and side-lying leg abduction. Three-dimensional mathematical models of the hip joint reaction force and the contact hip stress were used to simulate active exercises in different body positions. The absolute values of the hip joint reaction force and the peak contact hip stress are the highest in unsupported supine abduction (1.3 MPa) and in side-lying abduction (1.2 MPa), lower in upright abduction (0.5 MPa), and the lowest in supported supine abduction (0.2 MPa). All body positions the hip joint reaction force and the peak contact hip stress are the highest in the posterior-superior quadrant of acetabulum, followed by anterior-superior quadrant, posterior-inferior quadrant, and finally anterior-inferior quadrant. Spatial distribution of the average acetabular loading shows that early rehabilitation should be planned according to location of the fracture lines.

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“…A traditional method used to understand the control of limb movement involves the prediction of muscle forces or joint torques from EMG signals (e.g., Sanger 2007) that then serve as inputs to a biomechanical model of the limb to estimate resulting kinematics (Otten 1987;Soechting and Flanders 1997;Stein et al 1988;Valero-Cuevas et al 2003). Such a procedure is referred to as a forward approach.…”

Section: Forward Versus Inverse Approachesmentioning

confidence: 99%

“…Instead, most FES systems use a few stored programs of muscle stimulation, the playback of which is instigated and regulated by a control signal derived from a voluntary motor function retained by the patient (such as eye movements, shoulder movements, or electromyographic [EMG] activity detected from unaffected muscles). One possible approach to overcome this limitation would be to develop a generalized FES controller based on biomechanical models (e.g., Hatze 1980;Putnam 1991;Soechting and Flanders 1997;Stansfield et al 2003). The premise for using a biomechanical model is that a set of equations could be identified that characterizes the relationship between limb kinematics and muscle activity.…”

Section: Introductionmentioning

confidence: 99%

Probability-Based Prediction of Activity in Multiple Arm Muscles: Implications for Functional Electrical Stimulation

Anderson¹,

Fuglevand²

2008

Journal of Neurophysiology

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Anderson CV, Fuglevand AJ. Probability-based prediction of activity in multiple arm muscles: implications for functional electrical stimulation. J Neurophysiol 100: 482-494, 2008. First published April 24, 2008 doi:10.1152/jn.00956.2007. Functional electrical stimulation (FES) involves artificial activation of muscles with implanted electrodes to restore motor function in paralyzed individuals. The range of motor behaviors that can be generated by FES, however, is limited to a small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial difficulty associated with identifying the patterns of muscle stimulation needed to elicit specified movements. To overcome this limitation in controlling FES systems, we used probabilistic methods to estimate the levels of muscle activity in the human arm during a wide range of free movements based on kinematic information of the upper limb. Conditional probability distributions were generated based on hand kinematics and associated surface electromyographic (EMG) signals from 12 arm muscles recorded during a training task involving random movements of the arm in one subject. These distributions were then used to predict in four other subjects the patterns of muscle activity associated with eight different movement tasks. On average, about 40% of the variance in the actual EMG signals could be accounted for in the predicted EMG signals. These results suggest that probabilistic methods ultimately might be used to predict the patterns of muscle stimulation needed to produce a wide array of desired movements in paralyzed individuals with FES.

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Direct comparison of calculated hip joint contact forces with those measured using instrumented implants. An evaluation of a three-dimensional mathematical model of the lower limb

Cited by 141 publications

References 15 publications

Evaluation of predicted knee‐joint muscle forces during gait using an instrumented knee implant

Evaluation of predicted knee‐joint muscle forces during gait using an instrumented knee implant

Acetabular Loading in Active Abduction

Probability-Based Prediction of Activity in Multiple Arm Muscles: Implications for Functional Electrical Stimulation

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