Treatment and Prevention of Hip and Knee Arthritis (Organized by Canadian Orthopeadic Research Society) Organizers (original) (raw)
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A multivariate gait data analysis technique: Application to knee osteoarthritis
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 2004
Modern gait analysis is a powerful non-invasive tool for calculating the mechanical factors involved in pathological processes such as knee osteoarthritis (OA). Although very accurate measurements can be made, the clinical applicability and widespread use of gait analysis have been hindered by a lack of appropriate data analysis techniques for reducing and analysing the resulting large volumes of highly correlated gait data. This paper introduces a multidimensional galt data analysis technique that simultaneously considers multiple time-varying and discrete measures, exploiting the correlation structure between and within the measures. The multidimensional analysis technique was used to detect discriminatory mechanical features of knee OA gait patterns that involved interacting changes in several gait measures, at specific time portions of the gait cycle. The two most discriminatory features described a dynamic alignment difference and a loading response difference with knee OA.
Clinical Biomechanics, 2012
Background: Gait analysis is an important instrument in clinical research and results should be objective. The purpose of this study was to quantify clinical outcomes of two biomechanical models with different anatomical coordinate systems and angle decomposition strategies for knee joint kinematics. Methods: The study was designed to compare a functional approach and a predictive approach with a single comprehensive marker set. 10 healthy subjects and 12 subjects with knee osteoarthritis were analysed. Distinctive gait variables were averaged across five trials. Agreement between methods was illustrated with the so-called levels of agreement. Differences between models were quantified using a paired t-test or Wilcoxon-Signed Rank test in case of non-normality (Shapiro-Wilk test). Unpaired t-tests/Wilcoxon tests were used to compare gait variables between healthy subjects and subjects with knee osteoarthritis, and to examine whether statistical analysis of this comparison would yield different data interpretations when using different models. Findings: Outcome variables differed between the functional and predictive approaches in the sagittal plane (0.1-3.1°), and transverse plane (1.0-3.7°). With respect to the range of motion in the given movement plane, variables in the sagittal plane of the knee were more consistent between methods. The functional approach was more sensitive for detecting differences between groups for sagittal plane kinematics. Statistical analysis for transverse plane kinematics differed substantially between models. Interpretation: Sensitivity to detect differences of kinematic data between population groups can vary between biomechanical models. Rotational gait variables are inconsistent between models and should not be used as clinical outcome variables in daily routine.
IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2006
In this paper, a novel object classification method is introduced and developed within a biomechanical study of human knee function in which subjects are classified to one of two groups: subjects with osteoarthritic (OA) and normal (NL) knee function. Knee-function characteristics are collected using a three-dimensional motion-analysis technique. The classification method transforms these characteristics into sets of three belief values: a level of belief that a subject has OA knee function, a level of belief that a subject has NL knee function, and an associated level of uncertainty. The evidence from each characteristic is then combined into a final set of belief values, which is used to classify subjects. The final belief values are subsequently represented on a simplex plot, which enables the classification of a subject to be represented visually. The control parameters, which are intrinsic to the classification method, can be chosen by an expert or by an optimization approach. Using a leave-one-out cross-validation approach, the classification accuracy of the proposed method is shown to compare favorably with that of a well-established classifier-linear discriminant analysis. Overall, this study introduces a visual tool that can be used to support orthopaedic surgeons when making clinical decisions. Index Terms-Classification, Dempster-Shafer theory of evidence (DST), motion analysis, osteoarthritic (OA) knee function. I. INTRODUCTION P ATIENTS with movement disorders are occasionally referred to clinical motion-analysis laboratories. During brief assessments, a wealth of biomechanical data relating to the functional abnormality of a patient is collected, for example, range of movement, walking speed, etc. However, in the experience of the authors, it is extremely difficult to objectively analyze and gain conclusions from such a wealth of data. This is reinforced by the work of Benedetti et al., who commented that "it is often not easy for the clinician to examine so much data without a systematic and rigorous approach" [1, p. 204]. Chau [2], [3] presented extensive reviews of the techniques applied to the "formidable task" of gait-data analysis.
Data management in gait analysis for clinical applications
Clinical Biomechanics, 1998
Objective. To study the reliability of gait analysis data obtained using the Calibrated Anatomical System Technique (CAST) protocol and to verify the suitability and repeatability of the extraction of a number of parameters from the waveforms obtained. Design. The experimental protocol and the parametric analysis technique were applied on a population of able-bodied subjects. Background.
Quantitative comparison of five current protocols in gait analysis
Gait & Posture, 2008
Data collection and reduction procedures, coherently structured in protocols, are necessary in gait analysis to make kinematic and kinetic measurements clinically comprehensible. The current protocols differ considerably for the marker-set and for the biomechanical model implemented. Nevertheless, conventional gait variables are compared without full awareness of these differences.
Open Journal of Orthopedics, 2013
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.
Clinical Biomechanics, 2011
Background: Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Methods: Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Findings: Increased quadriceps and hamstring strength was observed following treatment (P b 0.05). Except for the knee flexion/extension angle, two different groups (G 1 and G 2 ) were extracted from the angle and moment data. When pre-and post-treatment analyses were performed considering the groups, participants exhibiting a G 2 knee moment pattern demonstrated a greater first peak flexion moment, lower adduction moment impulse and smaller rotation angle range post-treatment (P b 0.05). When pre-and post-treatment comparisons were performed without grouping, the data showed no treatment effect. Interpretation: The results of the present study suggest that the effect of physiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre-and post-treatment comparisons.
Biomechanical features of gait waveform data associated with knee osteoarthritis
Gait & Posture, 2007
This study compared the gait of 50 patients with end-stage knee osteoarthritis to a group of 63 age-matched asymptomatic control subjects. The analysis focused on three gait waveform measures that were selected based on previous literature demonstrating their relevance to knee osteoarthritis (OA): the knee flexion angle, flexion moment, and adduction moment. The objective was to determine the biomechanical features of these gait measures related to knee osteoarthritis. Principal component analysis was used as a data reduction tool, as well as a preliminary step for further analysis to determine gait pattern differences between the OA and the control groups. These further analyses included statistical hypothesis testing to detect group differences, and discriminant analysis to quantify overall group separation and to establish a hierarchy of discriminatory ability among the gait waveform features. The two groups were separated with a misclassification rate (estimated by cross-validation) of 8%. The discriminatory features of the gait waveforms were, in order of their discriminatory ability: the amplitude of the flexion moment, the range of motion of the flexion angle, the magnitude of the flexion moment during early stance, and the magnitude of the adduction moment during stance.
The human gait is a functional repetitive daily task and, it complies that altered pattern of movement occur with greater frequency, leading to repeated application of excessive stress on the musculoskeletal tissues predisposing to orthopedics lesions. The excessive internal rotation of the lower limbs related to the movement of excessive pronation of foot is an example of movements dysfunctions during the stance phase of gait. These inadequate movements of ankle-foot complex are accessed in ambulatory environment through qualitative analysis, which leads to inaccurate and subjective conclusions. In researches these movements are quantified by video photogrammetry, which due to the high cost of equipment prevents the clinician application. In this context, the objective of this study was to develop and validate a low cost device based in accelerometers and gyroscopes to quantitatively assess the movements of inversion and eversion of the calcaneus and internal and external rotation ...
2011
Background: The applicability of gait analysis has been implemented with the introduction of the principal component analysis (PCA), a statistical data reduction technique that allows the comparison of the whole cycle between groups of individuals. Objectives: Applying PCA, to compare the kinematics of the knee joint during gait, in the frontal and sagittal planes, between a group of elderly women with and without diagnosis in the initial and moderate stages of Osteoarthritis (OA). Methods: A total of 38 elderly women (69.6±8.1 years) with knee OA and 40 asymptomatic (70.3±7.7 years) participated on this study. The kinematics was obtained using the Qualisys Proreflex system. Results: The OA group showed decreased gait velocity and stride length (p<0.05) and was characterized with higher WOMAC pain score. In the frontal plane, the between-group differences of the components were not significant. In the sagittal plane, three principal components explained 99.7% of the data variance. Discriminant analysis indicated that component 2 and 3 could classify correctly 71.8% of the individuals. However, CP3, which captures the difference in the flexion knee angle magnitude during gait, was the variable with higher discrimination power between groups. Conclusions: PCA is an effective multivariate statistical technique to analyse the kinematic gait waveform during the gait cycle. The smaller knee flexion angle in the OA group was appointed as a discriminatory factor between groups, therefore, it should be considered in the physical therapy evaluation and treatment of elderly women with knee OA.