Predicting foot orthosis deformation based on its contour kinematics during walking.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 05 02 2020
accepted: 20 04 2020
entrez: 8 5 2020
pubmed: 8 5 2020
medline: 10 9 2020
Statut: epublish

Résumé

Customized foot orthoses (FOs) are designed based on foot posture and function, while the interaction between these metrics and FO deformation remains unknown due to technical problems. Our aim was to predict FO deformation under dynamic loading using an artificial intelligence (AI) approach, and to report the deformation of two FOs of different stiffness during walking. Each FO was fixed on a plate, and six triad reflective markers were fitted on its contour, and 55 markers on its plantar surface. Manual loadings with known magnitude and application point were applied to deform "sport" and "regular" (stiffer) FOs in all regions (training session). Then, 13 healthy male subjects walked with the same FOs inside shoes, where the triad markers were visible by means of shoe holes (walking session). The marker trajectories were recorded using optoelectronic system. A neural network was trained to find the dependency between the orientation of triads on FO contour and the position of markers on its plantar surface. After tuning hyperparameters and evaluating the performance of the model, marker positions on FOs surfaces were predicted during walking for each subject. Statistical parametric mapping was used to compare the pattern of deformation between two FOs. Overall, the model showed an average error of <0.6 mm for predicting the marker positions on both FOs. The training setup was appropriate to simulate the range of triads' displacement and the peak loading on FOs during walking. Sport FO showed different pattern and significantly higher range of deformation during walking compared to regular FO. Our technique enables an indirect and accurate estimation of FO surface deformation during walking. The AI model was capable to make a distinction between two FOs with different stiffness and between subjects. This innovative approach can help to optimally customize the FO design.

Sections du résumé

BACKGROUND
Customized foot orthoses (FOs) are designed based on foot posture and function, while the interaction between these metrics and FO deformation remains unknown due to technical problems. Our aim was to predict FO deformation under dynamic loading using an artificial intelligence (AI) approach, and to report the deformation of two FOs of different stiffness during walking.
METHODS
Each FO was fixed on a plate, and six triad reflective markers were fitted on its contour, and 55 markers on its plantar surface. Manual loadings with known magnitude and application point were applied to deform "sport" and "regular" (stiffer) FOs in all regions (training session). Then, 13 healthy male subjects walked with the same FOs inside shoes, where the triad markers were visible by means of shoe holes (walking session). The marker trajectories were recorded using optoelectronic system. A neural network was trained to find the dependency between the orientation of triads on FO contour and the position of markers on its plantar surface. After tuning hyperparameters and evaluating the performance of the model, marker positions on FOs surfaces were predicted during walking for each subject. Statistical parametric mapping was used to compare the pattern of deformation between two FOs.
RESULTS
Overall, the model showed an average error of <0.6 mm for predicting the marker positions on both FOs. The training setup was appropriate to simulate the range of triads' displacement and the peak loading on FOs during walking. Sport FO showed different pattern and significantly higher range of deformation during walking compared to regular FO.
CONCLUSION
Our technique enables an indirect and accurate estimation of FO surface deformation during walking. The AI model was capable to make a distinction between two FOs with different stiffness and between subjects. This innovative approach can help to optimally customize the FO design.

Identifiants

pubmed: 32379801
doi: 10.1371/journal.pone.0232677
pii: PONE-D-20-03374
pmc: PMC7205218
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0232677

Déclaration de conflit d'intérêts

It should also be mentioned that our innovative approach for quantifying the deformation of foot orthosis during walking does not provide any commercial asset at this point. Therefore, the materials presented in this manuscript is not related to any kind of consultancy, patents, products in development, or marketed products. Furthermore, our commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. Finally, we confirm that there is no competing of interest, but a fruitful collaboration between our research team and our industrial partners, Caboma and Medicus. On-going studies involving flat feet patients may provide commercial advantages.

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Auteurs

Maryam Hajizadeh (M)

Laboratory of Simulation and Movement Modelling, Institute of Biomedical Engineering, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada.

Benjamin Michaud (B)

Laboratory of Simulation and Movement Modelling, School of Kinesiology and Physical Activity Sciences, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada.

Gauthier Desmyttere (G)

Laboratory of Simulation and Movement Modelling, School of Kinesiology and Physical Activity Sciences, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada.

Jean-Philippe Carmona (JP)

Caboma, Montréal, Quebec, Canada.

Mickaël Begon (M)

Laboratory of Simulation and Movement Modelling, Institute of Biomedical Engineering, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada.
Laboratory of Simulation and Movement Modelling, School of Kinesiology and Physical Activity Sciences, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada.

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