Characterization Of Human Stability Using Vector Acceleration Signals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it