Preventive strategy of flatfoot deformity using fully automated procedure.

3D foot scans Abbreviations: Navicular height (NH) Confidence interval (CI) Finite element (FE) Finite element analysis (FEA), Computer-aided design (CAD) Foot orthoses Low arch Normalized Navicular Height Truncated Normalized navicular height (NNH) Normalized navicular height truncated (NNHt) Parametric modelling, Finite element analysis Standard deviation (SD) Standard error (SE)

Journal

Medical engineering & physics
ISSN: 1873-4030
Titre abrégé: Med Eng Phys
Pays: England
ID NLM: 9422753

Informations de publication

Date de publication:
09 2021
Historique:
received: 23 10 2020
revised: 26 03 2021
accepted: 04 07 2021
entrez: 4 9 2021
pubmed: 5 9 2021
medline: 21 10 2021
Statut: ppublish

Résumé

A non-invasive, no radiation, out-of-hospital automated system is proposed to identify low arch integrated in the design and manufacturing of personalized orthoses using parametric modelling. The aim of the design process is to integrate assistive technology with assessment and prevent low arch progressing to a more serious case - flatfoot. In the automated procedure, we developed an assessment method including reliable thresholds of foot type classification and test protocol to reduce interferences due to preceding activities, an automation to translate scanned data into parametric design for orthotic customization, finite element model evaluating effectiveness of the personalized design, and a personalized comparative test to evaluate the long-term improvement of foot arch shape. Our low arch threshold established by subject-specific 3D models reduced the misclassification rate from 55%, as previously reported to 6.9%. Individuals who engaged in sedentary activity (i.e. sitting) had the greater change in arch height compared to active activity (i.e. standing and walking), which is more likely to affect the obtained measure. Therefore, a test protocol now states that participants are not allowed to sit over 100 min prior the measurement to reduce such interference. We have proposed and tested an automated algorithm to translate scanned data including seven foot's parameters into customised parametric design of the insert. The method decreases the required time of orthotic computer-aided design from over 3 h to less than 2 min. A finite element analysis procedure was additionally developed to assess the performance of geometries and material of designed orthotic based on the distribution of plantar pressure and internal stress. The personalized comparative assessment based on midfoot contact area was carried out periodically for follow-up and the orthotic could be re-designed if necessary. The proposed automated procedure develops a pre-screening system to distinguish low arch and provide preventatives before it becomes symptomatic. Furthermore, non-symptom flatfoot can be detected at early stages and referred to medics for further diagnosis or treatment.

Identifiants

pubmed: 34479688
pii: S1350-4533(21)00082-5
doi: 10.1016/j.medengphy.2021.07.006
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

15-24

Informations de copyright

Copyright © 2021 IPEM. Published by Elsevier Ltd. All rights reserved.

Auteurs

Che-Wei Hu (CW)

School of Engineering, RMIT University, Australia; Department of Sport Science, University of Vienna, Austria. Electronic address: s3584609@student.rmit.edu.au.

Peter Dabnichki (P)

School of Engineering, RMIT University, Australia. Electronic address: peter.dabnichki@rmit.edu.au.

Arnold Baca (A)

Department of Sport Science, University of Vienna, Austria. Electronic address: arnold.baca@univie.ac.at.

Canh Toan Nguyen (CT)

School of Engineering, RMIT University, Australia. Electronic address: ncanhtoan@gmail.com.

Toh Yen Pang (TY)

School of Engineering, RMIT University, Australia. Electronic address: tohyen.pang@rmit.edu.au.

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