Dynamic Balance Measures and Sensing for Humans in Fall Detection and Prevention
Bibliographic record
Abstract
Balance Aid aims to enhance balance by detecting unstable postures and providing feedback to prevent a fall.This thesis presents the development of a shoe sensor, a method to detect anomalous gait behaviours, and a measure of dynamic balance.Ground reaction sensors must avoid disturbing natural gait.A lightweight, compliant, wearable shoe sensor was developed that measures ground reaction in real-time and can be used in fall detection and prevention.Falls can be prevented by reducing risk factors.An anomalous gait behaviour detection system was developed to detect fatigue, eyes-closed, and cluttered gait using force plates and the ground reaction sensors.In addition to reducing risky behaviours.Traditional measures of balance do not apply well to human gait and detect steps as falls.An angular momentum based measurement was found to be a dynamic measure of balance that is able to differentiate between a fall and a step.iii 5.13 The X (A/P) and Y (M/L) positions of the COP (red solid), COM (green dashed), and CMP (blue dotted) shown with respect to time for a side fall. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .A.1 The schematic for the conditioning and filtering circuits for the six-axis force and moment sensor . . . . . . . . . . . .
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".