Repeatability and reproducibility of OSSCA, a functional approach for assessing the kinematics of the lower limb

Taylor, William R.; Kornaropoulos, Evgenios; Duda, Georg N.; Kratzenstein, Stefan; Ehrig, Rainald; Arampatzis, Adamantios; Heller, Markus O.

doi:10.1016/j.gaitpost.2010.05.005

Cited by 76 publications

(69 citation statements)

References 29 publications

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“…While the approaches for kinematic and kinetic assessment vary considerably between motion laboratories, the skeletal kinematics (Charlton et al 2004;Ehrig et al 2006Ehrig et al , 2007Taylor et al 2010) have been developed (Peters et al 2010) and are now even integrated within commercial motion capture software (e.g. Vicon).…”

Section: Reference Movement Datamentioning

confidence: 99%

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Schellenberg

Taylor

Jonkers

et al. 2017

Computer Methods in Biomechanics and Biomedical Engineering

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Musculoskeletal modelling is widely used to estimate internal loading conditions. In order to optimise robustness and reduce errors between the subject-specific reference motion data (RMD) and the musculoskeletal simulation, 90 permutations of kinetic and kinematic data were analysed during split squats. A ranking for the scaling and kinematic weighting concepts based on the RMS errors when including functional centres of rotation (fCoRs), joint angles, and skin markers, revealed that analyses should include fCoR in the scaling and the simulation processes, as well as an automated weighting procedure including all attached skin markers for optimal registration of the musculoskeletal model to the RMD.

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Section: Reference Movement Datamentioning

confidence: 99%

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Schellenberg

Taylor

Jonkers

et al. 2017

Computer Methods in Biomechanics and Biomedical Engineering

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“…The marker set consisted of 46 markers: 6 on the pelvis, 8 on each thigh and shank, and 4 on each foot. 23 The complete procedure to determine the loading conditions is described in the following (Fig. 1).…”

Section: Motion Analysismentioning

confidence: 99%

“…23 In addition, each marker was weighted to minimize the residual error of the functional joint estimation 37 and maintain the anatomical constraints of segment lengths. The resulting nonlinear optimization problem had 84 unknowns (marker weights), was formulated using 12 distance error terms, and then solved using the active set algorithm from the KNITRO-library (Ziena Optimization LLC, Evanston, IL).…”

Section: Determination Of Lower Limb Motionmentioning

confidence: 99%

“…To ensure that the segment motions were appropriately tracked, the relative position of the functional joint centers and axes to the segment marker clusters were transferred from the reference motions to each of the four activities. This was achieved by fitting the weighted optimum common shape marker clusters 23,37,38 from the reference motions to the raw marker clusters of the specific activity for each segment, and transforming the functional joint axes and centers with them. This method thus provided reliable joint centers and axes for activities with insufficient range of motion to functionally determine these parameters.…”

Section: Determination Of Lower Limb Motionmentioning

confidence: 99%

“…Here, the kinematics of the limb play a key role. [21][22][23] Without a detailed comparison between model predictions and in vivo forces, it is difficult to unravel the complex interactions that govern the variation in the internal forces.…”

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confidence: 99%

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Patellofemoral joint contact forces during activities with high knee flexion

Trepczynski

Kutzner

Kornaropoulos

et al. 2011

Journal Orthopaedic Research

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The patellofemoral (PF) joint plays an essential role in knee function, but little is known about the in vivo loading conditions at the joint. We hypothesized that the forces at the PF joint exceed the tibiofemoral (TF) forces during activities with high knee flexion. Motion analysis was performed in two patients with telemetric knee implants during walking, stair climbing, sit-to-stand, and squat. TF and PF forces were calculated using a musculoskeletal model, which was validated against the simultaneously measured in vivo TF forces, with mean errors of 10% and 21% for the two subjects. The in vivo peak TF forces of 2.9-3.4 bodyweight (BW) varied little across activities, while the peak PF forces showed significant variability, ranging from less than 1 BW during walking to more than 3 BW during high flexion activities, exceeding the TF forces. Together with previous in vivo measurements at the hip and knee, the PF forces determined here provide evidence that peak forces across these joints reach values of around 3 BW during high flexion activities, also suggesting that the in vivo loading conditions at the knee can only be fully understood if the forces at the TF and the PF joints are considered together. ß

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Modulation of the Relationship Between External Knee Adduction Moments and Medial Joint Contact Forces Across Subjects and Activities

Trepczynski

Kutzner

Bergmann

et al. 2014

Arthritis & Rheumatology

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ObjectiveThe external knee adduction moment (EAM) is often considered a surrogate measure of the distribution of loads across the tibiofemoral joint during walking. This study was undertaken to quantify the relationship between the EAM and directly measured medial tibiofemoral contact forces (Fmed) in a sample of subjects across a spectrum of activities.MethodsThe EAM for 9 patients who underwent total knee replacement was calculated using inverse dynamics analysis, while telemetric implants provided Fmed for multiple repetitions of 10 activities, including walking, stair negotiation, sit-to-stand activities, and squatting. The effects of the factors “subject” and “activity” on the relationships between Fmed and EAM were quantified using mixed-effects regression analyses in terms of the root mean square error (RMSE) and the slope of the regression.ResultsAcross subjects and activities a good correlation between peak EAM and Fmed values was observed, with an overall R2 value of 0.88. However, the slope of the linear regressions varied between subjects by up to a factor of 2. At peak EAM and Fmed, the RMSE of the regression across all subjects was 35% body weight (%BW), while the maximum error was 127 %BW.ConclusionThe relationship between EAM and Fmed is generally good but varies considerably across subjects and activities. These findings emphasize the limitation of relying solely on the EAM to infer medial joint loading when excessive directed cocontraction of muscles exists and call for further investigations into the soft tissue–related mechanisms that modulate the internal forces at the knee.

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Repeatability and reproducibility of OSSCA, a functional approach for assessing the kinematics of the lower limb

Cited by 76 publications

References 29 publications

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Robustness of kinematic weighting and scaling concepts for musculoskeletal simulation

Patellofemoral joint contact forces during activities with high knee flexion

Modulation of the Relationship Between External Knee Adduction Moments and Medial Joint Contact Forces Across Subjects and Activities

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