2024-03-28T14:33:13Z
https://zenodo.org/oai2d
oai:zenodo.org:4893121
2021-06-02T13:53:34Z
user-cad-walk
user-eu
Brian G. Booth
Noel L. W. Keijsers
Jan Sijbers
2020-12-23
<p>Diagnosing foot complaints using plantar pressure videos is complicated by the presence of confounding factors (e.g. age, weight). Outlier detection could help with diagnosis, but these confounding factors result in data that is not independent and identically distributed (IID) with respect to a specific patient. To address this non-IID problem, we propose the modeling of confounding factors using metric learning. A distance metric is learned on the confounding factors in order to model their impact on the plantar pressures. This metric is then employed to weight plantar pressures from healthy controls when generating a patient-specific statistical baseline. Statistical parametric mapping is then used to compare the patient to this statistical baseline. We show that using metric learning reduces variance in these statistical baselines, which then improves the sensitivity of the outlier detection. These improvements in outlier detection get us one step closer to accurate computer-aided diagnosis of foot complaints.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 746614.
https://doi.org/10.1109/MIS.2020.3046431
oai:zenodo.org:4893121
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
IEEE Intelligent Systems, (2020-12-23)
anomaly detection
plantar pressure
metric learning
kernel regression
computer-aided diagnosis
Outlier detection for foot complaint diagnosis: modeling confounding factors using metric learning
info:eu-repo/semantics/article
oai:zenodo.org:3521582
2020-01-20T13:52:14Z
user-cad-walk
openaire
user-eu
Booth, Brian G.
2018-03-02
<p>The goal of the Individual Fellowships is to enhance the creative and innovative potential of experienced researchers, wishing to diversify their individual competence in terms of skill acquisition through advanced training, international and intersectoral mobility. In this presentation, we cover what this goal entails and how you can build your application with this goal in mind.</p>
https://doi.org/10.5281/zenodo.3521582
oai:zenodo.org:3521582
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521581
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
A Discussion on Impact: MSCA Individual Fellowships
info:eu-repo/semantics/lecture
oai:zenodo.org:3521612
2020-01-20T16:47:39Z
user-cad-walk
openaire
user-eu
Booth, Brian G.
2017-12-04
<p>“In the context of FOOTWORK, the project consortium wanted to develop an automated digital orthotics workflow consisting of digital patient measurement, computer-aided analysis, 3D modeling and digital fabrication of the orthoses,” <strong>says Toon Huysmans, Research Lead of the FOOTWORK project.</strong> “The aim was to apply, for the first time ever, a data-driven methodology using statistical foot models at each stage of the orthotics development. By developing such an automated digital process, the FOOTWORK project can strongly increase the efficiency and reliability of the orthotics workflow thereby ensuring the quality of the orthopedic shoes and corrective insoles.”</p>
https://doi.org/10.5281/zenodo.3521612
oai:zenodo.org:3521612
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521611
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
FootWork: Statistical Foot Modelling for a Digital Orthotics Workflow
info:eu-repo/semantics/lecture
oai:zenodo.org:3690729
2020-02-28T19:38:34Z
user-cad-walk
user-eu
Booth, Brian G.
Hoefnagels, Eva
Huysmans, Toon
Sijbers, Jan
Keijsers, Noel L. W.
2020-02-27
<p>Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship between plantar pressures in healthy controls and their demographic characteristics. This statistical model then serves as the healthy baseline to which an individual’s real plantar pressures are compared using statistical parametric mapping. As a proof-of-concept, we evaluated PAPPI on a cohort of 50 hallux valgus patients. PAPPI showed that plantar pressures from hallux valgus patients did not have a single, homogeneous pattern, but instead, 5 abnormal pressure patterns were observed in sections of this population. When comparing these patterns to foot pain scores (i.e. Foot Function Index, Manchester-Oxford Foot Questionnaire) and radiographic hallux angle measurements, we observed that patients with increased pressure under metatarsal 1 reported less foot pain than other patients in the cohort, while patients with abnormal pressures in the heel showed more severe hallux valgus angles and more foot pain. Also, incidences of pes planus were higher in our hallux valgus cohort compared to the modelled healthy controls. PAPPI helped to clarify recent discrepancies in group-level plantar pressure studies and showed its unique ability to produce quantitative, interpretable, and personalized analyses for plantar pressure images.</p>
This research has received funding from imec ICON grant no. 150218 and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.
https://doi.org/10.1371/journal.pone.0229685
oai:zenodo.org:3690729
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
PLoS One, 15(2), e0229685, (2020-02-27)
plantar pressure
personalized medicine
statistical parametric mapping
PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping
info:eu-repo/semantics/article
oai:zenodo.org:2598496
2020-01-24T19:25:36Z
user-cad-walk
openaire_data
user-uantwerpdatarepository
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2019-03-19
<p>This dataset contains the raw dynamic plantar pressure measurements of 50 Dutch individuals with Hallux Valgus collected prior to surgical intervention at Sint Maartenskliniek centres in the Netherlands. For each individual, between 8-15 dynamic plantar pressure measurements were collected from both feet. Also collected are walking speeds for each plantar pressure measurements, and demographic information of all individuals measured (age, height, weight, shoe size, sex, handedness, leg dominance). Finally, each participant also completed two foot self-assessment questionnaires: the Foot Function Index (FFI-5pt) and the Manchester-Oxford foot questionnaire. The score from these self-assessments are also provided.</p>
<p>For more information, please see the Readme.pdf file accompanying this dataset.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.2598496
oai:zenodo.org:2598496
eng
Zenodo
https://zenodo.org/communities/uantwerpdatarepository
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1441308
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
plantar pressure
pedobarography
dataset
dynamic
Hallux Valgus
The CAD WALK Hallux Valgus Dataset (Pre-Surgery)
info:eu-repo/semantics/other
oai:zenodo.org:3521552
2020-01-20T17:01:39Z
user-cad-walk
openaire
user-eu
Booth, Brian G.
2017-05-29
<p>The Flemish Scientific Research Funding Agency, together with the Belgian NCP's organized an information session over the 2017 call for Marie Skłodowska-Curie Actions (MSCA) Individual Fellowships (IF). The information session included invited speakers from both inside and outside academia to provide an understanding of, and preparation for, the Marie Skłodowska-Curie Actions Individual Fellowship.</p>
https://doi.org/10.5281/zenodo.3521552
oai:zenodo.org:3521552
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521551
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Marie Curie Individual Fellowships: Lessons Learned froma Successful Application
info:eu-repo/semantics/lecture
oai:zenodo.org:3521568
2020-01-20T16:48:14Z
user-cad-walk
openaire
user-eu
Booth, Brian G.
2017-09-20
<p>Dr. Brian Booth, lead researcher in the CAD WALK project, was invited to speak at the Expert Group Antwerp Molecular Imaging (EGAMI) workshop at the University of Antwerp. EGAMI is a multi-disciplinary group at the University of Antwerp whose goal is to enable the translation of molecular imaging research into industrial applications. It is hoped that by enabling this research translation, EGAMI will help improve clinical diagnoses and patient health.</p>
<p>The topic of the workshop was <em>"The Impact of Imaging Research"</em> and the invited speakers introduced various avenues that can increase research impact. The most commonly-mentioned themes included valorisation (the translation of research into products and services) and public outreach. Speakers also discussed various funding opportunities that encourage these activities.</p>
<p>"The EU's Marie Curie Actions strongly encourage the transfer of research into products", says Booth. "They do so by requiring their projects to have an international, inter-sector, and interdisciplinary focus. In that way, CAD WALK is a decent example of how this can be done. That being said, the valorisation aspect of CAD WALK has yet to begin, so my advice is pretty limited there".</p>
<p>While Dr. Booth had little to add on the topic of valorisation, he was more adamant about the need for researchers to increase public outreach efforts. "It is hard to get the average person to support what you do if they don't know who you are, don't feel that you care about them, and don't see the effort that you're putting in", says Booth. "We have to make a better effort to be accessible, transparent, and understandable".</p>
https://doi.org/10.5281/zenodo.3521568
oai:zenodo.org:3521568
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521567
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
CAD WALK: Computer-Aided Diagnosis of Foot Problems using Metric Learning
info:eu-repo/semantics/lecture
oai:zenodo.org:3521535
2020-01-20T16:20:21Z
user-cad-walk
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2019-08-02
<p>This study characterizes plantar pressure differences between hallux valgus patients and healthy controls using statistical parametric mapping (SPM). The plantar pressure differences are minimal which, combined with previous studies, suggest high variability in the plantar pressures of hallux valgus patients.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.3521535
oai:zenodo.org:3521535
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521534
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ISB/ASB 2019, The XXVII Conference of the International Socienty of Biomechanics and the American Socienty of Biomechanics, Calgary, Canada, 31 July - 4 August, 2019
Assessing Group Differences between Hallux Valgus Patients and Healthy Controls using Statistical Parametric Mapping
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3521520
2020-01-20T16:41:39Z
user-cad-walk
user-eu
Booth, Brian G.
Sijbers, Jan
Huysmans, Toon
2019-07-28
<p>The purpose of this pilot study was to evaluate whether machine learning algorithms can be used to design shoe lasts in an accurate fashion (i.e. sub-millimeter error).</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.3521520
oai:zenodo.org:3521520
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521519
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Footwear Science, 11(sup1), S17-S19, (2019-07-28)
FBS, Footwear Biomechanics Symposium, Kananaskis, Canada, 28-30 July 2019
A Machine Learning Approach to the Design of Customized Shoe Lasts
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3521486
2020-01-20T16:47:48Z
user-cad-walk
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2019-04-01
<p>While dynamic plantar pressure measurements are commonly used for clinical evaluation of gait-related problems, computational analysis techniques for these datasets are few and far between. To address this issue, we introduce an open-access database of plantar pressure videos for researchers to develop algorithms around.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.3521486
oai:zenodo.org:3521486
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521485
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ISBI, IEEE International Symposium on Biomedical Imaging, Venice, Italy, 8-11 April, 2019
Plantar pressure
Database
Biomechanics
Advancing Analysis Techniques for Plantar Pressure Videos via the CAD WALK Open-Access Database
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:3521478
2020-01-20T17:01:19Z
user-cad-walk
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Sijbers, Jan
Huysmans, Toon
2018-06-01
<p>Background</p>
<p>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.</p>
<p>Research question</p>
<p>We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.</p>
<p>Methods</p>
<p>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.</p>
<p>Results</p>
<p>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.</p>
<p>Significance</p>
<p>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.</p>
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.
https://doi.org/10.5281/zenodo.3521478
oai:zenodo.org:3521478
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521423
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Gait & Posture, 63, 268-275, (2018-06-01)
Pedobarography
Walking speed
Statistical parametric mapping
Spatiotemporal analysis
STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping
info:eu-repo/semantics/article
oai:zenodo.org:1441309
2020-01-24T19:25:05Z
user-cad-walk
openaire_data
user-uantwerpdatarepository
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2018-10-01
<p>This dataset contains the raw dynamic plantar pressure measurements of 16 Dutch individuals with Hallux Valgus collected prior to surgical intervention at Sint Maartenskliniek centres in the Netherlands. For each individual, 15 dynamic plantar pressure measurements were collected from both feet. Also collected are walking speeds for each plantar pressure measurements, and demographic information of all individuals measured (age, height, weight, shoe size, sex, handedness, leg dominance). Finally, each participant also completed two foot self-assessment questionnaires: the Foot Function Index (FFI-5pt) and the Manchester-Oxford foot questionnaire. The score from these self-assessments are also provided.</p>
<p>For more information, please see the Readme.pdf file accompanying this dataset.</p>
Note that this dataset is an initial, incomplete, release. Further data will be forthcoming as our study proceeds.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.1441309
oai:zenodo.org:1441309
eng
Zenodo
https://zenodo.org/communities/uantwerpdatarepository
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1441308
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
plantar pressure
pedobarography
dataset
dynamic
Hallux Valgus
The CAD WALK Hallux Valgus Data Pre-Release
info:eu-repo/semantics/other
oai:zenodo.org:3521455
2020-01-20T16:50:15Z
user-cad-walk
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Sijbers, Jan
Huysmans, Toon
2019-04-18
<p>Data reduction techniques are commonly applied to dynamic plantar <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/pressure-measurement">pressure measurements</a>, often prior to the measurement’s analysis. In performing these data reductions, information is discarded from the measurement before it can be evaluated, leading to unkonwn consequences. In this study, we aim to provide the first assessment of what impact data reduction techniques have on plantar pressure measurements. Specifically, we quantify the extent to which information of any kind is discarded when performing common data reductions. Plantar pressure measurements were collected from 33 healthy controls, 8 <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/hallux-valgus">Hallux Valgus</a> patients, and 10 <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/metatarsalgia">Metatarsalgia</a> patients. Eleven common data reductions were then applied to the measurements, and the resulting <a href="https://www.sciencedirect.com/topics/engineering/dataset">datasets</a> were compared to the original measurement in three ways. First, information theory was used to estimate the information content present in the original and reduced datasets. Second, <a href="https://www.sciencedirect.com/topics/engineering/principal-component-analysis">principal component analysis</a> was used to estimate the number of intrinsic dimensions present. Finally, a permutational multivariate ANOVA was performed to evaluate the significance of group differences between the healthy controls, Hallux Valgus, and Metatarsalgia groups. The evaluated data reductions showed a minimum of 99.1% loss in information content and losses of <a href="https://www.sciencedirect.com/topics/engineering/dimensionality">dimensionality</a> between 20.8% and 83.3%. Significant group differences were also lost after each of the 11 data reductions (alpha = 0.05), but these results may differ for other patient groups (especially those with highly-deformed footprints) or other region of interest definitions. Nevertheless, the existence of these results suggest that the diagnostic content of dynamic plantar pressure measurements is yet to be fully exploited.</p>
This research has received funding from the European Union's Horizon 2020 reserch and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.
https://doi.org/10.5281/zenodo.3521455
oai:zenodo.org:3521455
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521454
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Journal of Biomechanics, 87, 161-166, (2019-04-18)
Pedobarography
Information content
Dimensionality
Data reductions
An assessment of the information lost when applying data reduction techniques to dynamic plantar pressure measurements
info:eu-repo/semantics/article
oai:zenodo.org:4041946
2020-09-22T00:26:58Z
user-cad-walk
openaire
user-eu
Brian G Booth
2020-09-16
<p>"When our feet hurt, we hurt all over" -Socrates</p>
<p>Over four years at the University of Antwerp, my colleagues and I have looked into the modelling of normal foot shape and dynamics through the use of 3D optical scanning and dynamic plantar pressure measurements. The aim was to statistically model these normal measurements and use then as a baseline to which we can compare the measurements of those with foot complaints. Using various outlier detection algorithms, we employ these statistical model to identify plantar pressures or foot shape features that are statistically abnormal. The assumption here is that the statistical outliers we identify have some relation to the foot complaints a person is experiencing. Over multiple studies, we have been able to confirm this assumption over populations of hallux valgus patients as well as patients with abnormal foot arches. Finally, we use this information in machine learning algorithms to estimate custom orthopedic shoe lasts for patients with foot conditions.</p>
https://doi.org/10.5281/zenodo.4041946
oai:zenodo.org:4041946
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.4041945
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
plantar pressure
3D foot shape
statistical modelling
outlier detection
machine learning
The Serious Study of Silly Walks
info:eu-repo/semantics/lecture
oai:zenodo.org:3521591
2020-01-20T16:48:13Z
user-cad-walk
openaire
user-eu
Booth, Brian G.
2018-01-23
<p>This presentation on the CAD WALK analysis techniques was presented to the InForMed consortium meeting January 23rd, 2018.</p>
<p>In the InForMed project, an integrated pilot line for medical devices will be established, covering the complete innovation chain from technology concept to system qualification. It will include micro-fabrication, assembly and even the fabrication of smart catheters. Uniquely, the integrated pilot line is hosted by a large industrial end-user, and is specifically targeted and equipped to bridge the gap in the landscape of micro-fabrication of medical devices between concept creation and full-scale production.</p>
<p>39 Partners from 10 countries participate in the project to form manufacturing networks and an eco-system where new medical devices van be seeded and nurtured to grow into new business opportunities for Europe, in a time when there is a paradigm shift from large expensive diagnostic equipment towards small, disposable, minimal invasive and un-obtrusive diagnostic and therapeutic instruments and tools.</p>
<p>The Pilot line will be demonstrated by six demonstrator products which cover traditional, emerging, and entirely new market segments, in the domains of "Hospital and Heuristic Care as well as "Home Care and Well-being," and that demonstrate the trend towards "Smart Health" solutions.</p>
<p>InForMed is an ECSEL JU project and is co-funded by grants from Belgium, Finland, France, Germany, Great Britain, Ireland, the Netherlands, Spain, Sweden and Switzerland.</p>
https://doi.org/10.5281/zenodo.3521591
oai:zenodo.org:3521591
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521590
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Innovative Analysis Techniques of Pressure Data
info:eu-repo/semantics/lecture
oai:zenodo.org:3521424
2020-01-20T16:47:22Z
user-cad-walk
user-eu
Brian G. Booth
Noel L.W. Keijsers
Jan Sijbers
Toon Huysmans
2018-06-01
<p>Background</p>
<p>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.</p>
<p>Research question</p>
<p>We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions.</p>
<p>Methods</p>
<p>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.</p>
<p>Results</p>
<p>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.</p>
<p>Significance</p>
<p>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.</p>
This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 746614.
https://doi.org/10.5281/zenodo.3521424
oai:zenodo.org:3521424
eng
Zenodo
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.3521423
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Gait & Posture, 63, 268-275, (2018-06-01)
Pedobarography
Walking speed
Statistical parametric mapping
Spatiotemporal analysis
STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping
info:eu-repo/semantics/article
oai:zenodo.org:1265420
2020-01-24T19:25:26Z
user-cad-walk
openaire_data
user-uantwerpdatarepository
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2018-06-04
<p>This dataset contains the raw dynamic plantar pressure measurements of 55 healthy Dutch individuals collected at Sint Maartenskliniek, Nijmegen. For each individual, 24 dynamic plantar pressure measurements were collected from both feet. Also collected are walking speeds for each plantar pressure measurements, and demographic information of all individuals measured (age, height, weight, shoe size, sex, handedness, leg dominance).</p>
<p>For more information, please see the Readme.pdf file accompanying this dataset.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.1265420
oai:zenodo.org:1265420
Zenodo
https://zenodo.org/communities/uantwerpdatarepository
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1265419
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
plantar pressure
pedobarography
dataset
dynamic
healthy controls
The CAD WALK Healthy Controls Dataset
info:eu-repo/semantics/other
oai:zenodo.org:3406523
2020-01-24T19:25:05Z
user-cad-walk
openaire_data
user-uantwerpdatarepository
user-eu
Booth, Brian G.
Keijsers, Noel L.W.
Huysmans, Toon
Sijbers, Jan
2019-03-19
<p>This dataset contains the raw dynamic plantar pressure measurements of 50 Dutch individuals with Hallux Valgus collected prior to surgical intervention at Sint Maartenskliniek centres in the Netherlands. For each individual, between 8-15 dynamic plantar pressure measurements were collected from both feet. Also collected are walking speeds for each plantar pressure measurements, and demographic information of all individuals measured (age, height, weight, shoe size, sex, handedness, leg dominance). Additionally, x-ray images were used to measure the hallux valgus angle and intermetatarsal angle for each hallux valgus case. Finally, each participant also completed two foot self-assessment questionnaires: the Foot Function Index (FFI-5pt) and the Manchester-Oxford foot questionnaire. The score from these self-assessments are also provided.</p>
<p>For more information, please see the Readme.pdf file accompanying this dataset.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746614.
https://doi.org/10.5281/zenodo.3406523
oai:zenodo.org:3406523
eng
Zenodo
https://zenodo.org/communities/uantwerpdatarepository
https://zenodo.org/communities/cad-walk
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.1441308
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
plantar pressure
pedobarography
dataset
dynamic
Hallux Valgus
The CAD WALK Hallux Valgus Dataset (Pre-Surgery)
info:eu-repo/semantics/other