ArticleSensors (Basel, Switzerland)2020
A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- A Hierarchical Machine Learning-Based Framework for Clinical Decision Support in Foot Orthosis Prescription: Algorithm Development and Validation Study.JMIR medical informatics · 2026Observational
- Influence of Foot Morphology on Postural Sway During Childhood.Annals of biomedical engineering · 2026Article
- SoleFusion-Net: an explainable multimodal deep learning framework for diabetic foot syndrome classification in type II diabetes mellitus.Scientific reports · 2026Article
- Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data.Scientific reports · 2026Article
- Footprint to prediction: low-cost, three-class foot arch screening using a Harris mat.Frontiers in artificial intelligence · 2026Article
- Toward Smarter Orthopedic Care: Classifying Plantar Footprints from RGB Images Using Vision Transformers and CNNs.Journal of imaging · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- A novel deep learning approach to classify 3D foot types of diabetic patients.Scientific reports · 2025Article
- Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data.Frontiers in bioengineering and biotechnology · 2025Article
- A Novel Deep Transfer Learning-Based Approach for Automated Pes Planus Diagnosis Using X-ray Image.Diagnostics (Basel, Switzerland) · 2023Article
- Labeling of Baropodometric Analysis Data Using Computer Vision Techniques in Classification of Foot Deformities.Medicina (Kaunas, Lithuania) · 2023Article
- Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods.Sensors (Basel, Switzerland) · 2023Article
- Decision Tree-Based Foot Orthosis Prescription for Patients with Pes Planus.International journal of environmental research and public health · 2022Article
- Estimation of Various Walking Intensities Based on Wearable Plantar Pressure Sensors Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- A Geometrical Divide of Data Particle in Gravitational Classification of Moons and Circles Data Sets.Entropy (Basel, Switzerland) · 2020Article
- Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review.Journal of orthopaedicsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The human foot is easily deformed owing to the innate form of the foot or an incorrect walking posture. Foot deformations not only pose a threat to foot health but also cause fatigue and pain when walking; therefore, accurate diagnoses of foot deformations are required. However, the measurement of foot deformities requires specialized personnel, and the objectivity of the diagnosis may be insufficient for professional medical personnel to assess foot deformations. Thus, it is necessary to develop an objective foot deformation classification model. In this study, a model for classifying foot types is developed using image and numerical foot pressure data. Such heterogeneous data are used to generate a fine-tuned visual geometry group-16 (VGG16) and K-nearest neighbor (k-NN) models, respectively, and a stacking ensemble model is finally generated to improve accuracy and robustness by combining the two models. Through k-fold cross-validation, the accuracy and robustness of the proposed method have been verified by the mean and standard deviation of the f1 scores (0.9255 and 0.0042), which has superior performance compared to single models generated using only numerical or image data. Thus, the proposed model provides the objectivity of diagnosis for foot deformation, and can be used for analysis and design of foot healthcare products.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.