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

Reid, Susan A.; Graham, Ryan B.; Costigan, Patrick A.

doi:10.1016/j.gaitpost.2009.10.005

Cited by 72 publications

(68 citation statements)

References 35 publications

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“…Another typical application of PCA in gait analysis is to investigate and summarize group differences in gait waveform data (e.g., Deluzio et al, 1997;Muniz & Nadal, 2009;Reid et al, 2010;Yamamoto et al, 1983). In these applications, one PCA is often conducted for each joint/muscle/segment using a subjects × time matrix, where the subjects mode is a collection of subjects from different groups (e.g., Astephen et al, 2008;Chester & Wrigley, 2008;Cochran et al, 1984;Donoghue et al, 2008;Lamoth et al, 2006;Lauer et al, 2005;Lee et al, 2009;McKean et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three-or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lowerlimb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).ii To my loving and supportive family.iii

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Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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“…Time-series parameterization techniques were applied to each waveform to extract the instantaneous values of amplitude and its relative time [24,28]. This technique is considered one of the most common methods of gait data analysis, which is preferable, and clinically acceptable [29,30].…”

Section: Methodsmentioning

confidence: 99%

“…SWDA was performed using the Wilks' lambda method with the default setting criteria of the F value to enter is at least 0.05 and F value to remove is less than 0.10. Features within the range of F values are statistically significance of groups discrimination [29,34]. Both statistical analyses were performed using the IBM SPSS Statistics version 21.0 (IBM, New York, USA).…”

Section: Features Selectionmentioning

confidence: 99%

Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces

Hasan¹,

Jailani²,

Tahir³

et al. 2017

TELKOMNIKA

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“…Population modelling has also been used in conjunction with discriminant analysis to identify biomechanical features of dynamic gait waveforms associated with ageing [24] and pathology [25,26]. Although useful for the classification of normal or pathological gait, the weights obtained from a principal component analysis can also be used to synthetically generate feasible gait data across a larger population than what was originally sampled [27].…”

Section: Population Models and The Musculoskeletal Atlas Projectmentioning

confidence: 99%

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Fernandez

Heidlauf

et al. 2016

Interface Focus.

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This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

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

Cited by 72 publications

References 35 publications

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

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