Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear
Foot diseases such as plantar fasciitis and flat feet occur in millions of people worldwide, resulting in mobility problems and serious health complications. This paper presents an AI-based approach that uses machine learning to identify foot ailments, classify foot conditions, and suggest the right...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11062646/ |
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| author | Sonaa Rajagopal Muralikrishnan Mani Shyam Venkatraman R. Suganya |
| author_facet | Sonaa Rajagopal Muralikrishnan Mani Shyam Venkatraman R. Suganya |
| author_sort | Sonaa Rajagopal |
| collection | DOAJ |
| description | Foot diseases such as plantar fasciitis and flat feet occur in millions of people worldwide, resulting in mobility problems and serious health complications. This paper presents an AI-based approach that uses machine learning to identify foot ailments, classify foot conditions, and suggest the right footwear. The approach integrates clinical gait analysis, symptom diagnosis, and customized footwear suggestions with synthetically generated images, which aid in the production of the footwear. The proposed work is based on three large datasets: (1) Grayscale pressure sensor heat maps for foot posture, with high-resolution foot pressure maps that capture weight distribution and posture; (2) Clinically Validated Foot Condition Dataset, comprising foot conditions verified by physiotherapists and linked to real symptoms; and (3) Footwear Recommendation Dataset for Specific Foot Conditions, with expert-curated footwear suggestions tailored to various foot conditions for optimal support and comfort. The framework consists of three modules: a CNN-based VGG-16-GRU model which identifies gait posture based on pressure sensor heatmaps, an autoencoder-based random forest model which classifies foot diseases based on the detected gait posture, and an LSTM-ensembled XGBoost model which recommends features of suggested footwear. The recommended footwear features obtained from this model is then synthetically generated to visually perceive the suggested footwear. The experimental results show excellent performance with detecting and recommending shoe designs. Additionally, Stable Diffusion-based synthetically generated footwear images provide improved personalization and recommendations for footwear. This study proposes a method to design customized shoes for foot conditions by leveraging AI-generated designs, biomechanical analyses, and material optimization. |
| format | Article |
| id | doaj-art-4e47d4095c95439496de819cedf7508f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e47d4095c95439496de819cedf7508f2025-08-20T03:28:44ZengIEEEIEEE Access2169-35362025-01-011311488011490010.1109/ACCESS.2025.358496611062646Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized FootwearSonaa Rajagopal0https://orcid.org/0009-0001-4572-6863Muralikrishnan Mani1https://orcid.org/0009-0005-0731-8534Shyam Venkatraman2https://orcid.org/0009-0001-9902-9879R. Suganya3https://orcid.org/0000-0003-1874-6479School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaFoot diseases such as plantar fasciitis and flat feet occur in millions of people worldwide, resulting in mobility problems and serious health complications. This paper presents an AI-based approach that uses machine learning to identify foot ailments, classify foot conditions, and suggest the right footwear. The approach integrates clinical gait analysis, symptom diagnosis, and customized footwear suggestions with synthetically generated images, which aid in the production of the footwear. The proposed work is based on three large datasets: (1) Grayscale pressure sensor heat maps for foot posture, with high-resolution foot pressure maps that capture weight distribution and posture; (2) Clinically Validated Foot Condition Dataset, comprising foot conditions verified by physiotherapists and linked to real symptoms; and (3) Footwear Recommendation Dataset for Specific Foot Conditions, with expert-curated footwear suggestions tailored to various foot conditions for optimal support and comfort. The framework consists of three modules: a CNN-based VGG-16-GRU model which identifies gait posture based on pressure sensor heatmaps, an autoencoder-based random forest model which classifies foot diseases based on the detected gait posture, and an LSTM-ensembled XGBoost model which recommends features of suggested footwear. The recommended footwear features obtained from this model is then synthetically generated to visually perceive the suggested footwear. The experimental results show excellent performance with detecting and recommending shoe designs. Additionally, Stable Diffusion-based synthetically generated footwear images provide improved personalization and recommendations for footwear. This study proposes a method to design customized shoes for foot conditions by leveraging AI-generated designs, biomechanical analyses, and material optimization.https://ieeexplore.ieee.org/document/11062646/Convolutional neural network (CNN)gated recurrent unit (GRU)long short-term memory (LSTM)XGBoostautoencoderrandom forest |
| spellingShingle | Sonaa Rajagopal Muralikrishnan Mani Shyam Venkatraman R. Suganya Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear IEEE Access Convolutional neural network (CNN) gated recurrent unit (GRU) long short-term memory (LSTM) XGBoost autoencoder random forest |
| title | Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear |
| title_full | Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear |
| title_fullStr | Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear |
| title_full_unstemmed | Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear |
| title_short | Personalized Solutions for Foot Health: Machine Learning-Based Foot Condition Detection, Classification, and Recommendation of Customized Footwear |
| title_sort | personalized solutions for foot health machine learning based foot condition detection classification and recommendation of customized footwear |
| topic | Convolutional neural network (CNN) gated recurrent unit (GRU) long short-term memory (LSTM) XGBoost autoencoder random forest |
| url | https://ieeexplore.ieee.org/document/11062646/ |
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