STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping

•Hypothesis: subsampling plantar pressure video leads to loss of gait information.•Thus, we introduce STAPP as a methodology to analyze full plantar pressure videos.•Comparison of STAPP with pSPM and COP analysis (all fit the SPM framework).•Evaluation of plantar pressure differences due to changes...

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Published inGait & posture Vol. 63; no. NA; pp. 268 - 275
Main Authors Booth, Brian G., Keijsers, Noël L.W., Sijbers, Jan, Huysmans, Toon
Format Journal Article
LanguageEnglish
Published England Elsevier B.V 01.06.2018
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ISSN0966-6362
1879-2219
1879-2219
DOI10.1016/j.gaitpost.2018.04.029

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Abstract •Hypothesis: subsampling plantar pressure video leads to loss of gait information.•Thus, we introduce STAPP as a methodology to analyze full plantar pressure videos.•Comparison of STAPP with pSPM and COP analysis (all fit the SPM framework).•Evaluation of plantar pressure differences due to changes in walking speed.•Subsampling in pSPM and COP led to under-reporting of pressure differences. Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos – a task which led to the discarding of gait information in our study – can be avoided using STAPP.
AbstractList •Hypothesis: subsampling plantar pressure video leads to loss of gait information.•Thus, we introduce STAPP as a methodology to analyze full plantar pressure videos.•Comparison of STAPP with pSPM and COP analysis (all fit the SPM framework).•Evaluation of plantar pressure differences due to changes in walking speed.•Subsampling in pSPM and COP led to under-reporting of pressure differences. Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos – a task which led to the discarding of gait information in our study – can be avoided using STAPP.
Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.
Highlights: Hypothesis: subsampling plantar pressure video leads to loss of gait information. Thus, we introduce STAPP as a methodology to analyze full plantar pressure videos. Comparison of STAPP with pSPM and COP analysis (all fit the SPM framework). Evaluation of plantar pressure differences due to changes in walking speed. Subsampling in pSPM and COP led to under-reporting of pressure differences. Abstract: Background: Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. Research question We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. Methods: To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. Results: As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. Significance We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.
Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures.BACKGROUNDPedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures.We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.RESEARCH QUESTIONWe hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds.METHODSTo test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds.As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques.RESULTSAs walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques.We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.SIGNIFICANCEWe therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP.
Author Keijsers, Noël L.W.
Booth, Brian G.
Huysmans, Toon
Sijbers, Jan
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Keywords Pedobarography
Statistical parametric mapping
Walking speed
Spatiotemporal analysis
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Snippet •Hypothesis: subsampling plantar pressure video leads to loss of gait information.•Thus, we introduce STAPP as a methodology to analyze full plantar pressure...
Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of...
Highlights: Hypothesis: subsampling plantar pressure video leads to loss of gait information. Thus, we introduce STAPP as a methodology to analyze full plantar...
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StartPage 268
SubjectTerms Pedobarography
Spatiotemporal analysis
Statistical parametric mapping
Walking speed
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Title STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping
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