@mastersthesis {2271,
	title = {Quantitative assessment of 3D foot shape using statistical shape analysis},
	volume = {PhD in Sciences},
	year = {2022},
	month = {10/2022},
	type = {PhD thesis},
	author = {Kristina Stankovi{\'c}}
}
@inproceedings {2100,
	title = {Statistical shape and pose model of the forearm for custom splint design},
	booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
	year = {2021},
	author = {Femke Danckaers and Jeroen Van Houtte and Brian G Booth and Frederik Verstreken and Jan Sijbers}
}
@article {2017,
	title = {PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping},
	journal = {PlosOne},
	volume = {15},
	year = {2020},
	pages = {e0229685},
	doi = {https://doi.org/10.1371/journal.pone.0229685},
	author = {Brian G Booth and Eva Hoefnagels and Toon Huysmans and Jan Sijbers and No{\"e}l L.W. Keijsers}
}
@article {2070,
	title = {Small medial femoral condyle morphotype is associated with medial compartment degeneration and distinct morphological characteristics: a comparative pilot study},
	journal = {Knee Surgery, Sports Traumatology, Arthroscopy},
	year = {2020},
	doi = {https://doi.org/10.1007/s00167-020-06218-8},
	author = {Jonas Grammens and Annemieke Van Haver and Femke Danckaers and Brian G Booth and Jan Sijbers and Peter Verdonk}
}
@article {2030,
	title = {Subject-specific identification of three dimensional foot shape deviations using statistical shape analysis},
	journal = {Expert Systems With Applications},
	volume = {151},
	year = {2020},
	pages = {1-11},
	doi = {https://doi.org/10.1016/j.eswa.2020.113372},
	author = {Kristina Stankovi{\'c} and Toon Huysmans and Femke Danckaers and Jan Sijbers and Brian G Booth}
}
@conference {1924,
	title = {Advancing Analysis Techniques for Plantar Pressure Videos via Open-Access Contributions},
	year = {2019},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Toon Huysmans and Jan Sijbers}
}
@inproceedings {1854,
	title = {An Articulating Statistical Shape Model of the Human Hand},
	booktitle = {Advances in Human Factors in Simulation and Modeling (AHFE 2018)},
	volume = {780},
	year = {2019},
	pages = {433{\textendash}445},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents a registration framework for the construction of a statistical shape model of the human hand in a standard pose. It brings a skeletonized reference model of an individual human hand into correspondence with optical 3D surface scans of hands by sequentially applying articulation-based registration and elastic surface registration. Registered surfaces are then fed into a statistical shape modelling algorithm based on principal component analysis. The model-building technique has been evaluated on a dataset of optical scans from 100 healthy individuals, acquired with a 3dMD scanning system. It is shown that our registration framework provides accurate geometric and anatomical alignment, and that the shape basis of the resulting statistical model provides a compact representation of the specific population. The model also provides insight into the anatomical variation of the lower arm and hand, which is useful information for the design of well-fitting products.},
	isbn = {978-3-319-94223-0},
	doi = {10.1007/978-3-319-94223-0_41},
	author = {Jeroen Van Houtte and Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Bertrand, V{\'e}ronique and Frederik Verstreken and Jan Sijbers and Toon Huysmans},
	editor = {Cassenti, Daniel N.}
}
@article {1927,
	title = {An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements},
	journal = {Journal of Biomechanics },
	volume = {87},
	year = {2019},
	pages = {161-166},
	doi = {https://doi.org/10.1016/j.jbiomech.2019.02.008},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@article {1837,
	title = {STAPP: SpatioTemporal Analysis of Plantar Pressure Measurements using Statistical Parametric Mapping},
	journal = {Gait and Posture},
	volume = {3},
	year = {2018},
	pages = {268-275},
	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 have 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.},
	doi = {10.1016/j.gaitpost.2018.04.029},
	author = {Brian G Booth and No{\"e}l L.W. Keijsers and Jan Sijbers and Toon Huysmans}
}
@article {1838,
	title = {Three-dimensional quantitative analysis of healthy foot shape: a proof of concept study},
	journal = {Journal of Foot and Ankle Research},
	volume = {11},
	year = {2018},
	month = {Mar},
	pages = {1-13},
	abstract = {Background
Foot morphology has received increasing attention from both biomechanics researches and footwear manufacturers. Usually, the morphology of the foot is quantified by 2D footprints. However, footprint quantification ignores the foot{\textquoteright}s vertical dimension and hence, does not allow accurate quantification of complex 3D foot shape.

Methods
The shape variation of healthy 3D feet in a population of 31 adult women and 31 adult men who live in Belgium was studied using geometric morphometric methods. The effect of different factors such as sex, age, shoe size, frequency of sport activity, Body Mass Index (BMI), foot asymmetry, and foot loading on foot shape was investigated. Correlation between these factors and foot shape was examined using multivariate linear regression.

Results
The complex nature of a foot{\textquoteright}s 3D shape leads to high variability in healthy populations. After normalizing for scale, the major axes of variation in foot morphology are (in order of decreasing variance): arch height, combined ball width and inter-toe distance, global foot width, hallux bone orientation (valgus-varus), foot type (e.g. Egyptian, Greek), and midfoot width. These first six modes of variation capture 92.59\% of the total shape variation. Higher BMI results in increased ankle width, Achilles tendon width, heel width and a thicker forefoot along the dorsoplantar axis. Age was found to be associated with heel width, Achilles tendon width, toe height and hallux orientation. A bigger shoe size was found to be associated with a narrow Achilles tendon, a hallux varus, a narrow heel, heel expansion along the posterior direction, and a lower arch compared to smaller shoe size. Sex was found to be associated with differences in ankle width, Achilles tendon width, and heel width. Frequency of sport activity was associated with Achilles tendon width and toe height.

Conclusion
A detailed analysis of the 3D foot shape, allowed by geometric morphometrics, provides insights in foot variations in three dimensions that can not be obtained from 2D footprints. These insights could be applied in various scientific disciplines, including orthotics and shoe design.},
	issn = {1757-1146},
	doi = {10.1186/s13047-018-0251-8},
	url = {https://doi.org/10.1186/s13047-018-0251-8},
	author = {Kristina Stankovi{\'c} and Brian G Booth and Femke Danckaers and Fien Burg and Vermaelen, Philippe and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
@inproceedings {1695,
	title = {Foot Abnormality Mapping using Statistical Shape Modelling},
	booktitle = {7th International Conference and Exhibition on 3D Body Scanning Technologies (3DBST)},
	year = {2016},
	pages = {70-79},
	edition = {Lugano, Switzerland, 30 November - 1 December},
	doi = {doi:10.15221/16.070},
	author = {Kristina Stankovi{\'c} and Femke Danckaers and Brian G Booth and Fien Burg and Saartje Duerinck and Jan Sijbers and Toon Huysmans}
}
