Statistical Parametric Mapping
Re-imagining biomechanical data analysis through spm1d, waveform (1D) analysis and statistics
Key collaborators:
A. Prof. Todd Pataky - Kyoto University, Japan
A. Prof. Jos Vanrenterghem - KU Leuven, Belgium
This research theme primarily focuses on the integration of statistical parametric mapping into biomechanics through the use of open-source package spm1d (www.spm1d.org, Pataky 2012). Arguably our most influential paper is Pataky et al. (2013) which introduces vector-field analysis (multivariate statistical analysis of waveforms) to biomechanics. Other technical spm1d papers address issues including non-parametric inference, regions-of-interest, EMG, and waveform homogeneity.
Other research has addressed biomechanical data analysis more broadly by demonstrating the likelihood of false positives with 0D data analysis and 1D power analysis. This work has also branched into complementary methods including Functional Data Analysis and Principal Component Analysis through collaboration with Dr. John Warmenhoven.
Publications
Simultaneously assessing amplitude and temporal effects in biomechanical trajectories using nonlinear registration and statistical nonparametric mapping
Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis
PCA of waveforms and functional PCA: A primer for biomechanics
On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data
A force profile analysis comparison between functional data analysis, statistical parametric mapping and statistical non-parametric mapping in on-water single sculling
A computational framework for estimating statistical power and planning hypothesis-driven experiments involving one-dimensional biomechanical continua
Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power
The probability of false positives in zero-dimensional analyses of one-dimensional kinematic, force and EMG trajectories
Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis
Can two-dimensional measured peak sagittal plane excursions during drop vertical jumps help identify three-dimensional measured joint moments?
Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series
Two-way ANOVA for scalar trajectories, with experimental evidence of non-phasic interactions
Vector field statistics for objective center-of-pressure trajectory analysis during gait, with evidence of scalar sensitivity to small coordinate system rotations
Vector field statistical analysis of kinematic and force trajectories
