A simple model to analyze the effectiveness of linear time normalization to reduce variability in human movement analysis

Page, Álvaro; Epifanio, Irene

doi:10.1016/j.gaitpost.2006.01.006

Cited by 12 publications

(12 citation statements)

References 9 publications

(11 reference statements)

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“…Figure 5), suprathreshold SPM{t} or SPM{F} clusters can be order-ofmagnitude larger than potential registration errors and one may thus appropriately ignore small misregistrations. Nonetheless, homologous data registrability is essential to all SPM analyses, so continued scrutiny of biomechanical curve registration (Sadeghi et al 2003;Page and Epifanio 2007;Thies et al 2009) is necessary to help to elucidate SPM's validity for particular applications.…”

Section: Discussionmentioning

confidence: 99%

One-dimensional statistical parametric mapping in Python

Pataky

2012

Computer Methods in Biomechanics and Biomedical Engineering

587

377

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Statistical parametric mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of 'SPM1D', a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky. net/spm1d/.

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Section: Discussionmentioning

confidence: 99%

One-dimensional statistical parametric mapping in Python

Pataky

2012

Computer Methods in Biomechanics and Biomedical Engineering

587

377

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“…Finally, time-warping functions were assessed for nonlinearity. Page and Epifanio (2007) found that time normalization adequately reduces phase variability when a linear relationship between individual phase durations and total movement duration exists. Therefore, if warping functions are linear, then registration may not offer any additional advantage over time normalization.…”

Section: Registration Analysismentioning

confidence: 96%

Effect of registration on cyclical kinematic data

Crane

Cassidy

Rothman

et al. 2010

Journal of Biomechanics

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Given growing interest in Functional Data Analysis (FDA) as a useful method for analyzing human movement data, it is critical to understand the effects of standard FDA procedures, including registration, on biomechanical analyses. Registration is used to reduce phase variability between curves while preserving the individual curves shape and amplitude. The application of three methods available to assess registration could benefit those in the biomechanics community using FDA techniques: comparison of mean curves, comparison of average RMS values, and assessment of time-warping functions. Therefore, the present study has two purposes. First, the necessity of registration applied to cyclical data after time normalization is assessed. Second, we illustrate the three methods for evaluating registration effects. Masticatory jaw movements of 22 healthy adults (2 males, 21 females) were tracked while subjects chewed a gum-based pellet for 20 seconds. Motion data were captured at 60 Hz with two gen-locked video cameras. Individual chewing cycles were time normalized and then transformed into functional observations. Registration did not affect mean curves and warping functions were linear. Although registration decreased the RMS, indicating a decrease in inter-subject variability, the difference was not statistically significant. Together these results indicate that registration may not always be necessary for cyclical chewing data. An important contribution of this paper is the illustration of three methods for evaluating registration that are easy to apply and useful for judging whether the extra data manipulation is necessary.

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“…Nevertheless, these are limitations of registration and not of SPM per se. Continued biomechanical registration scrutiny (Sadeghi et al, 2000(Sadeghi et al, , 2003Duhamel et al, 2004;Page and Epifanio, 2007) may help to clarify SPM's appropriateness for specific applications.…”

Section: Limitationsmentioning

confidence: 99%