DAGAN-Based Gait Features Augmentation for Ankle Instability Detection
Bibliographic record
Abstract
We developed a computer-aided program for the diagnosis of ankle ligament injury. This study used the Heidelberg Foot Measurement Method (HFMM) to obtain the specific gait kinematic characteristics. To address the issue of insufficient available medical samples, an Attention Dual Generative Adversarial Network (Attention-DualGAN) is proposed to augment the gait feature set. We used a Convolutional Long Short-Term Memory (ConvLSTM) Network-based and Time Convolution Network (TCN)-based detection model to observe the impact of the extended dataset. The results show that the accuracy of the ConvLSTM model after data enhancement improves by 2.4%, and the accuracy of the TCN model after data enhancement improves by 7.47%. Through the visualization of Pearson correlation coefficient, histogram, and scatter plot, it is proved that Attention-DualGAN produces high-quality gait kinematics characteristics. This study shows that Attention-DualGAN generates synthetic gait features with high correlation to real data, and the performance of the detection model in the diagnosis of ankle ligament injury can be improved by adding appropriate synthetic data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".