Characterization Of Human Stability Using Vector Acceleration Signals
Bibliographic record
Abstract
Biomedical signals carry information about a physiological event. The part of the signal pertaining to a specific event is called an epoch. Once the event has been determined, the corresponding waveform may be segmented and analyzed based on many parameters[1]. As falls have increased in recent years due to an aging population, it is important to gain insight to the reaction of an individual to perturbations. One common method of studying human reaction is by using a balance aperture. This thesis describes the physical actions that produce acceleration on a balance apparatus and captures the acceleration on an accelerometer. Algorithms were developed to segment the unstable periods of the accelerometer signal. Wavelets were used as well as non-linear filters. The non-linear filters increased the amplitudes of periods of instability, simple signal models of the output of the non-linear filters where formulated and analyzed. Vector processing techniques were also developed. The experimental results demonstrate that the acceleration during unstable periods can be differentiated by its frequency content, by its discontinuous nature and by using vector relationships. The algorithms were tested with five individuals and had over 80% accuracy.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".