Discriminative time-frequency kernels for gait analysis for amyotrophic lateral sclerosis
Bibliographic record
Abstract
Many stochastic systems show certain trends which in turn govern their underlying non-stationary time varying behavior. In order to facilitate efficient quantification of such signals, their analysis necessitates the use of robust tools for discerning between different classes of data. Research show that, use of time-frequency techniques offer intelligible representations for non-stationary signals, along with facilitating computation of instantaneous parameters. Further, in order to obtain efficient discrimination machine learning (ML) modules are often used alongside suitable representation techniques. In this work, we exploit the concepts of ML-kernel functions directly by incorporating them in the ambiguity time-frequency (TF) space, thereby obtaining a one-step discrimination between different non-stationary patterns. The proposed technique is evaluated for quantification applications for gait signal analysis. An overall classification accuracy of 93.1% is reported for the neurological gait database consisting of signals from 16-control and 13-amyotrophic lateral sclerosis (ALS) subjects. Results indicate that this scheme offers great potential in designing robust tools for time-varying signal analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".