Biomedical subjects

J L Astephen

Publications and source records attributed to J L Astephen.

3 recordsLinked to original sources

PubMed2006

Biomechanical features of gait waveform data associated with knee osteoarthritis: an application of principal component analysis.

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.

Aged↗

PubMed2005

Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique.

BACKGROUND: Osteoarthritis of the knee is related to many correlated mechanical factors that can be measured with gait analysis. Gait analysis results in large data sets. The analysis of these data is difficult due to the correlated, multidimensional nature of the measures. METHODS: A multidimensional model that uses two multivariate statistical techniques, principal component analysis and discriminant analysis, was used to discriminate between the gait patterns of the normal subject group and the osteoarthritis subject group. Nine time varying gait measures and eight discrete measures were included in the analysis. All interrelationships between and within the measures were retained in the analysis. FINDINGS: The multidimensional analysis technique successfully separated the gait patterns of normal and knee osteoarthritis subjects with a misclassification error rate of <6%. The most discriminatory feature described a static and dynamic alignment factor. The second most discriminatory feature described a gait pattern change during the loading response phase of the gait cycle. INTERPRETATION: The interrelationships between gait measures and between the time instants of the gait cycle can provide insight into the mechanical mechanisms of pathologies such as knee osteoarthritis. These results suggest that changes in frontal plane loading and alignment and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis gait patterns. Subsequent investigations earlier in the disease process may suggest the importance of these factors to the progression of knee osteoarthritis.

Adaptation, Physiological↗

PubMed2004

A multivariate gait data analysis technique: application to knee osteoarthritis.

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 gait 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.

Aged↗