Brian G Booth1, Noël L W Keijsers2, Jan Sijbers3, Toon Huysmans4. 1. imec-Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium. Electronic address: brian.booth@uantwerpen.be. 2. Department of Research, Sint Maartenskliniek, P.O. Box 9011, 6500 GM Nijmegen, The Netherlands. Electronic address: n.keijsers@maartenskliniek.nl. 3. imec-Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium. Electronic address: jan.sijbers@uantwerpen.be. 4. imec-Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, B-2610 Antwerp, Belgium; Section on Applied Ergonomics & Design, Department of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628 CE Delft, The Netherlands. Electronic address: T.Huysmans@tudelft.nl.
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.
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.