Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics

Fitzpatrick, Clare K.; Baldwin, Mark A.; Rullkoetter, Paul J.; Laz, Peter J.

doi:10.1016/j.jbiomech.2010.08.016

Cited by 53 publications

(50 citation statements)

References 41 publications

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“…The sensitivity of joint force components due to individual muscle impairments were quantified by means 142 of PCA (Fitzpatrick et al, 2011). As mentioned before, weakness of each muscle group was simulated with 200 143 probabilistic trials in which individual muscle strength variables (F 0 ) were reduced simultaneously.…”

Section: Principal Component Analysis (Pca) 141mentioning

confidence: 99%

How human gait responds to muscle impairment in total knee arthroplasty patients: Muscular compensations and articular perturbations

Ardestani

Moazen

2016

Journal of Biomechanics

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Post-surgical muscle weakness is prevalent among patients who undergo total knee arthroplasty (TKA).We conducted a probabilistic multi-body dynamics (MBD) to determine whether and to what extent habitual gait patterns of TKA patients may accommodate strength deficits in lower extremity muscles. We analyzed muscular and articular compensations in response to various muscle impairments, and the minimum muscle strength requirements needed to preserve TKA gait patterns in its habitual status.Muscle weakness was simulated by reducing the strength parameter of muscle models in MBD analysis.Using impaired models, muscle and joint forces were calculated and compared versus those from baseline gait i.e.

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Section: Principal Component Analysis (Pca) 141mentioning

confidence: 99%

How human gait responds to muscle impairment in total knee arthroplasty patients: Muscular compensations and articular perturbations

Ardestani

Moazen

2016

Journal of Biomechanics

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“…Monte Carlo (MC) method, an effective technique for simple structural response for probabilistic analysis [12][13][14], is unsuitable due to unacceptable computational efficiency for the nonlinear dynamic, multi-object and multi-discipline (MOMD) of complex machinery. Many attempts to solve the effects of the uncertainties have led to the development of the other probabilistic analysis method-response surface method (RSM, also called surrogate model), and it was widely employed in many fields [15][16][17][18][19][20]. Currently, RSM is effective to continuously improve the accuracy and efficiency in structural reliability by reducing the number of expensive finite element analysis [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Fei

Choy

et al. 2016

Nonlinear Dyn

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To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine bladetip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.

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“…In the registration, FE analysis is incorporated into their registration algorithm to constrain the registration and accomplish anatomically acceptable alignment. Fitzpatrick et al [72] developed a method for analysis implanted patellofemoral mechanics by combining a probabilistic and PCA approach. Their approach describes the relationship between input parameters and biomechanical output measurements.…”

Section: Orthopedicsmentioning

confidence: 99%

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka

Nakamoto

Sato

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

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Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics

Cited by 53 publications

References 41 publications

How human gait responds to muscle impairment in total knee arthroplasty patients: Muscular compensations and articular perturbations

How human gait responds to muscle impairment in total knee arthroplasty patients: Muscular compensations and articular perturbations

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

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