Context-Aware Hard and Slow Fall Detection
Bibliographic record
Abstract
Fall is one of the main causes of injuries for the elderly and fall detection (FD) for senior monitoring has received considerable attention from both the academic community and healthcare industries. In recent years, there has been an increasing interest in using wearable sensors, such as accelerometers to monitor the subject's body movement and apply Machine Learning (ML) methods to detect and prevent falls. Since it is extremely difficult to collect accelerometer data of real falls during activities of daily living (ADL), researchers tended to rely on simulating falls in well-protected environments. They collect ADL separately, applied ML algorithms to classify falls and ADL, and reported very high FD accuracy rates. However, these studies cannot be applied in a real fall context. In this paper, instead of classifying ADL and fall online, we propose to incorporate fall data within ADL data to obtain more realistic datasets and apply ML to detect falls online. Several ML algorithms including CatBoost (CB), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB), were applied to the datasets. Experimental results show a fall detection accuracy of 88.70 %. We also extend our work to cover slow falls which, to the best of our knowledge, was not extensively addressed in previous works.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".