ArticleJournal of neuroengineering and rehabilitation2022
Deep learning approach to estimate foot pressure distribution in walking with application for a cost-effective insole system.
Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 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
- Multimodal Feature-Level Fusion CBAM U-Net for Static Plantar Pressure Prediction Using Plantar Geometry and Sparse Anatomical Landmarks.Sensors (Basel, Switzerland) · 2026Article
- Robust and anti-swelling MXene composite hydrogel sensors for intelligent gait monitoringRSC advances · 2026Article
- Deep Convolutional Neural Network-Based Detection of Gait Abnormalities in Parkinson's Disease Using Fewer Plantar Sensors in a Smart Insole.Biosensors · 2026Article
- From Static to Dynamic: Complementary Roles of FSR and Piezoelectric Sensors in Wearable Gait and Pressure Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Fiber Bragg Grating Embedded 3D-Printed Insole with Commercial and Portable Reader for Stance Phase Determination.Biosensors · 2025Article
- Skating efficiency and technique during roller speed skate using innovative piezoelectric smart socks: an exploratory study.Frontiers in sports and active living · 2025Article
- Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.Sensors (Basel, Switzerland) · 2024Article
- Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems.Bioengineering (Basel, Switzerland) · 2024Article
- A review on contemporary nanomaterial-based therapeutics for the treatment of diabetic foot ulcers (DFUs) with special reference to the Indian scenario.Nanoscale advances · 2022Review
- Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.Sensors (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
backgroundFoot pressure distribution can be used as a quantitative parameter for evaluating anatomical deformity of the foot and for diagnosing and treating pathological gait, falling, and pressure sores in diabetes. The objective of this study was to propose a deep learning model that could predict pressure distribution of the whole foot based on information obtained from a small number of pressure sensors in an insole.
methodsTwenty young and twenty older adults walked a straight pathway at a preferred speed with a Pedar-X system in anti-skid socks. A long short-term memory (LSTM) model was used to predict foot pressure distribution. Pressure values of nine major sensors and the remaining 90 sensors in a Pedar-X system were used as input and output for the model, respectively. The performance of the proposed LSTM structure was compared with that of a traditionally used adaptive neuro-fuzzy interference system (ANFIS). A low-cost insole system consisting of a small number of pressure sensors was fabricated. A gait experiment was additionally performed with five young and five older adults, excluding subjects who were used to construct models. The Pedar-X system placed parallelly on top of the insole prototype developed in this study was in anti-skid socks. Sensor values from a low-cost insole prototype were used as input of the LSTM model. The accuracy of the model was evaluated by applying a leave-one-out cross-validation.
resultsCorrelation coefficient and relative root mean square error (RMSE) of the LSTM model were 0.98 (0.92 ~ 0.99) and 7.9 ± 2.3%, respectively, higher than those of the ANFIS model. Additionally, the usefulness of the proposed LSTM model for fabricating a low-cost insole prototype with a small number of sensors was confirmed, showing a correlation coefficient of 0.63 to 0.97 and a relative RMSE of 12.7 ± 7.4%.
conclusionsThis model can be used as an algorithm to develop a low-cost portable smart insole system to monitor age-related physiological and anatomical alterations in foot. This model has the potential to evaluate clinical rehabilitation status of patients with pathological gait, falling, and various foot pathologies when more data of patients with various diseases are accumulated for training.
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