A Deep-Learning Approach for Foot-Type Classification Using Heterogeneous Pressure Data
| Autor: | Yoojeong Noh, Young-Jin Kang, Jonghyeok Chae |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Posture 02 engineering and technology Walking lcsh:Chemical technology Biochemistry Article Analytical Chemistry Foot type 03 medical and health sciences 0302 clinical medicine Deep Learning stomatognathic system 0202 electrical engineering electronic engineering information engineering Arch index Humans lcsh:TP1-1185 Electrical and Electronic Engineering Foot pressure Instrumentation fine-tuned VGG16 030203 arthritis & rheumatology business.industry Foot stacking ensemble Pressure data Deep learning technology industry and agriculture k-NN Pattern recognition heterogeneous pressure data arch index Atomic and Molecular Physics and Optics 020201 artificial intelligence & image processing Artificial intelligence business human activities |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 4481, p 4481 (2020) Sensors Volume 20 Issue 16 |
| ISSN: | 1424-8220 |
| Popis: | 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&minus 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. |
| Databáze: | OpenAIRE |
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