Estimating Dynamic Plantar Pressure Distribution from Wearable Inertial Sensors Using a Hybrid CNN-BiLSTM Architecture
Bibliographic record
Abstract
Purpose Plantar pressure distribution is a crucial indicator in gait analysis, with significant value in clinical diagnoses and sports optimization. Traditional measurement methods, however, are often limited by expensive equipment and laboratory settings. This study aimed to develop an accurate, portable, and cost-effective method using a deep learning model based on data from wearable Inertial Measurement Units to predict comprehensive plantar pressure distributions. Methods This study aims to develop a portable deep learning model based on Inertial Measurement Units data to predict plantar pressure distribution. We propose a model that combines Convolutional Neural Network and Bidirectional Long Short-Term Memory, where Convolutional Neural Network extracts local features from Inertial Measurement Units data, Bidirectional Long Short-Term Memory captures the temporal dependencies of the gait cycle, an attention mechanism optimizes the prediction of key time steps, and body weight information is integrated to accommodate individual differences. Results Experimental results show that in 10-fold cross-validation, the model achieves a Mean Squared Error of 0.98 and a Structural Similarity Index of 0.89, demonstrating excellent prediction accuracy and distribution similarity. Conclusions This study provides a cost-effective method for plantar pressure analysis, which is expected to be integrated into wearable devices for real-time gait monitoring, with applications in rehabilitation and sports optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".