Postural Sway Detection using Kolmogorov-Arnold Network as Siamese Model
Bibliographic record
Abstract
Continuous monitoring of postural sway is crucial for safeguarding elderly individuals and patients with neurological conditions, such as Parkinson’s disease, as their balance is significantly impacted. This continuous monitoring is achieved using triaxial accelerometer sensors that provide time series signals and are used to train models to detect the postural sway of elderly individuals or patients with neurological conditions. However, the available postural sway dataset has a limited number of samples, and the performance of trained models deteriorates when the accelerometer signal is noisy. A Siamese network Kolmogorov-Arnold Network (Siamese-KAN) is pro-posed to address this issue. This network can be trained with a few samples from each class. Training of the Siamese network is conducted by flattening the bispectrum of each accelerometer channel and then fusing the magnitudes to construct a single input vector. Various similarity functions are employed along with contrastive loss to train the Siamese network. Additionally, a Siamese network with multi-layer perceptron (MLP) is also similarly trained for comparison purposes. Preliminary results show that the Siamese-KAN model achieves better accuracy with clean signals than the Siamese-MLP. However, when the signal is noisy, the Siamese-KAN model outperforms the Siamese-MLP using Euclidean and Manhattan similarity functions, while the Siamese-MLP performs better with Cosine and Bray-Curtis similarity functions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".