Wearable Technology for Fall Prevention-Data Collection Method and Hardware Optimization
Bibliographic record
Abstract
One of the most severe health risks to elderly populations is the risk of falling. Falls can lead to acute injuries, correlating with a decline in physical health. Recognizing the significance of this issue, the Fall Prevention Project aims to develop a method that can accurately predict and prevent falls in elderly demographics. Data collection is essential to understanding and predicting falls. This article addresses the data collection methods used in the Fall Prevention Project. For this project, a gyroscope and accelerometer sensor embedded in a watch records data. By collecting information to train a machine learning algorithm, patient stability can be modeled to reduce the likelihood of injuries and fatalities in at-risk patients. Recently, notable advancements have taken place in the development of prototypes for both the software and hardware components of the watch utilized in the Fall Prevention Project. Changes to the watch's hardware include replacing the previous micro controller with a controller that requires less power, decreasing the overall size of the watch, and adding a power switch. Changes in software include formatting improvements, the implementation of Excel to store data, and the removal of errors. These developments mark significant progress in refining the technology and functionality of the watch prototype.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".