Postural Sway Classification using Modified Vision Transformer
Bibliographic record
Abstract
Continuous monitoring of postural sway in elderly individuals and patients with neurodegenerative diseases is crucial for fall prevention, thereby reducing mortality and morbidity rates. This monitoring can be accomplished through the use of accelerometers. A modified vision transformer (MViT) with patch partition (PP), local self-attention (LSA), and residual connection (RC) is proposed. In this study, the signal magnitude vector (smv) derived from triaxial accelerometer is employed as input to the proposed modified vision transform (MViT) for classifying postural sways into one of the following classes: standing, anteroposterior (AP), medio-lateral (ML), and unstable. PP is carried out using a rectangle window of length (N) and an overlap ratio (α). The role played by the window length and the overlap ratio on the classification performance are studied. Additionally, various augmentation methods are implemented and used in conjunction with PP to improve the classification performance. MViT that has PP implemented using smaller window and larger overlap when trained with augmented data yields the best classification. These results demonstrate that MViT and augmentation improve classification performance and augmentation without PP is inferior to the improvements achieved using a MViT with PP without augmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".