Gait assessment in unicompartmental knee arthroplasty patients: Principal component modelling of gait waveforms and clinical status

Deluzio, Kevin J.; Wyss, Urs P.; Costigan, Patrick A.; Sorbie, Charles; Zee, Benny

doi:10.1016/s0167-9457(99)00030-5

Cited by 53 publications

(47 citation statements)

References 23 publications

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“…Only a few studies have examined gait patterns in TKA using waveform techniques, several studies use principal component analysis (PCA) to examine gait, and their results indicate that PCA can effectively identify differences in kinetic waveforms at the hip for TKA versus control subjects [2][3][4][5][6]. This study emphasized the need for statistically-based methods by which one can discriminate and classify subjects based on the entire waveform.…”

Section: Introductionmentioning

confidence: 99%

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and Principal Component Analysis

Roy

Chester²,

McGibbon³

2013

OJO

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The use of parameterization in assessing gait waveforms has been widely accepted, although it is recognized that this approach excludes the majority of information contained in the waveform. Waveform analysis techniques, such as principal component analysis (PCA), have gained popularity in recent years as a more effective approach to extracting important information from human movement waveforms, but are more challenging to interpret. Few studies have compared these two different approaches to determine which yields the most relevant information. This study compared the kinematic patterns during gait of six total knee arthroplasty (TKA) subjects (10 TKA knees), to a group of 10 agematched asymptomatic control subjects (19 control knees). An eight-camera Vicon M-cam system was used to track movement and compute joint angles. Group differences in parameterization (max and min peaks) values and principal component scores were tested using one-way ANOVA and Kruskal-Wallis tests. Using parameterization, the TKA group was characterized by reduced hip extension, increased hip flexion, increased anterior pelvic tilt, increased trunk tilt, and reduced sagittal ankle angles compared to the control group. Waveform analysis, by means of PCA, showedmagnitude shifts in sagittal ankle waveforms between groups, rather than solely reporting differences in peaks. Waveform analysis also indicated a significant shift in the magnitude of the entire waveform for hip angles, pelvic tilt, and trunk tilt, indicating no change in range of motion between groups, but rather a change in the way in which range of motion is achieved at the hip. This study has identified several gait variables that were significantly different between the TKA and control groups. Our results suggest that waveform analysis is effective at identifying magnitude shifts as sources of variability between groups, which would not necessarily be analyzed using conventional parameterization techniques unless one knew a priori where the variability would exist.

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Section: Introductionmentioning

confidence: 99%

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and Principal Component Analysis

Roy

Chester²,

McGibbon³

2013

OJO

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“…These include: Fourier series, cluster analysis, neural network classifiers and principal component analysis (PCA) [14][15][16][17][18][19]. A technique such as PCA uses the entire waveform in its analysis and may provide better information regarding the quantitative and temporal differences between groups [14,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…PCA works by exploiting the correlation structure of the dataset, thus the greater the correlation, the greater the potential for data reduction. Recently, PCA has detected differences in gait waveforms between normal, pathological and age-related gait patterns during walking [14,20,21,23,26].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of young and older adult stair climbing gait using principal component analysis

2010

Self Cite

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“…PCA has been widely used in the field of Biomechanics to describe continuous waveforms (1), (2).Their applications includes fields such diverse as gait analysis (1,(3)(4)(5)(6)(7), equilibrium control (8), coordination of thumb joints (9), analysis of lifting techniques (10, number of discrete variables (almost a thousand), which then needs a high number of PC's to explain a representative percentage of the original variance. On the other hand, in these applications, PCA is used from a multivariate perspective: a finite set of discrete variables are obtained from one or several continuous time series by sampling at arbitrary time intervals.…”

Section: Introductionmentioning

confidence: 99%

Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

Epifanio

Ávila

Page

et al. 2008

Med Biol Eng Comput

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This paper presents an application of Functional Principal Component Analysis (FPCA) to describe inter-subject variability of multiple waveforms. This technique was applied to the study of sit-to-stand movement in two groups of people, osteoarthritic patients and healthy subjects.Although STS movement has not been much applied to the study of knee osteoarthritis, it can provide relevant information about the effect of osteoarthritis disease on knee joint function.Two waveforms, knee flexion angle and flexion moment, were simultaneously analysed. Instead of using the common multivariate approach we used the functional one, which allows working with continuous functions without neither discretization nor time scale normalization.The results show that time-scale normalization can alter the FPCA solution. Furthermore, FPCA presents a better discriminatory power compared to the classical multivariate approach. Then, this technique can be applied as a functional assessment tool, allowing the identification of relevant variables to discriminate heterogeneous groups, such as healthy and pathological subjects.

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Gait assessment in unicompartmental knee arthroplasty patients: Principal component modelling of gait waveforms and clinical status

Cited by 53 publications

References 23 publications

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and Principal Component Analysis

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and Principal Component Analysis

Differentiation of young and older adult stair climbing gait using principal component analysis

Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

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