Gait characteristics as a function of age and gender

Nigg, Benno M.; Fisher, Veronica; Ronsky, J.

doi:10.1016/0966-6362(94)90106-6

Cited by 94 publications

(73 citation statements)

References 14 publications

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“…These were, for instance, an increased range of motion in the arm movement (Elble et al, 1991) and in the plantar-dorsiflexion of the foot (Nigg et al, 1994). In the same manner, differences in individual body part movements could be observed by examining individual markers over time.…”

Section: Discussionmentioning

confidence: 67%

Marker-based classification of young–elderly gait pattern differences via direct PCA feature extraction and SVMs

Eskofier

Federolf

Kugler

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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This file was dowloaded from the institutional repository Brage NIH -brage.bibsys.no/nih Eskofier, B. M., Federolf, P., Kugler, P. F., Nigg, B. (2013 AbstractThe classification of gait patterns has great potential as a diagnostic tool, for example for injury diagnostic or to identify at-risk gait in the elderly. This paper has the purpose of presenting a method for classifying gait pattern group differences using the complete spatial and temporal information of the segment motion quantified by markers. The obtained classification rates are compared to previous studies using conventional classification features.For our analysis, 37 three-dimensional marker trajectories were collected from each of our 24 young and 24 elderly female subjects while they were walking on a treadmill. Principal component analysis was performed on these trajectories to retain the spatial and temporal information in the markers. Using a SupportVector Machine with linear kernel, a classification rate of 95.8% was obtained.This classification approach also allowed visualization of the contribution of individual markers to group differentiation in position and time.The approach made no specific assumptions and did not require prior knowledge of specific time points in the gait cycle. It is therefore directly applicable for group classification tasks in any study involving marker measurements.

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Section: Discussionmentioning

confidence: 67%

Marker-based classification of young–elderly gait pattern differences via direct PCA feature extraction and SVMs

Eskofier

Federolf

Kugler

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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“…Gait pattern analyses have been addressed to reveal kinematic, kinetic and electromyographic (EMG) gait characteristic for modeling human walking [1][2][3]. The discovery of gait pattern will help to identify any change of gait that reflects the gait degeneration due to pathological reasons.…”

Section: Introductionmentioning

confidence: 99%

Mining Gait Pattern for Clinical Locomotion Diagnosis Based on Clustering Techniques

Zhang

Begg

2006

Advanced Data Mining and Applications

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Abstract. Scientific gait (walking) analysis provides valuable information about an individual's locomotion function, in turn, to assist clinical diagnosis and prevention, such as assessing treatment for patients with impaired postural control and detecting risk of falls in elderly population. While several artificial intelligence (AI) paradigms are addressed for gait analysis, they usually utilize supervised techniques where subject groups are defined a priori. In this paper, we explore to investigate gait pattern mining with clustering-based approaches, in which k-means and hierarchical clustering algorithms are employed to derive gait pattern. After feature selection and data preparation, we conduct clustering on the constructed gait data model to build up pattern-based clusters. The centroids of clusters are then treated as the subject profiles to model the various kinds of gait pattern, e.g. normal or pathological. Experiments are undertaken to visualize the derived subject clusters, evaluate the quality of clustering paradigm in terms of silhouette and mean square error and compare the results with the discovery derived from hierarchy tree analysis. In addition, analysis conducted on test data demonstrates the usability of the proposed paradigm in clinical applications.

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“…Loss of balance during walking, including tripping and slipping, has been related to specific phases of gait, such as the swing or double support phases, respectively (Patla, 1997). Norms for the various subcomponents of gait, including walking speed and step length, have been developed for men and women of different ages (Andriacchi, Ogle & Galante, 1977;Blanks & Hageman, 1989: Cunningham, Paterson, Himann et al, 1993Dobbs, Charlett, Bowes et al, 1993;Hageman & Blanke, 1986;Himann, Cunningham, Rechnitzer et al, 1988;Murray, 1967;Murray, Kory & Clarkson, 1969;Nigg, Fisher & Ronsky, 1994;Nigg & Skleryk, 1988;O'Brien, Power, Sanford et al, 1983). Walking is usually assessed at the customary walking pace o f the subject, or self selected walking velocity (SSWV).…”

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confidence: 99%