Efficient Fall Detection using Bidirectional Long Short-Term Memory
Bibliographic record
Abstract
Falls are one of the most common causes of injury among the elderly. As a result, fall detection has received in the last decade considerable attention from both academia and the healthcare industry. Accelerometer data, collected from simulated falls, were widely used with classical machine learning (ML) algorithms as well as with threshold-based methods to identify fall situations that can be used to launch an alert for help. As collecting real fall data is challenging, most of the research papers on fall detection have used limited data which do not reflect the complexity of real fall situations. Fortunately, a comprehensive fall dataset called “Simulated Falls and Daily Living Activities Dataset” has recently become available. This dataset includes 1827 simulated falls of 20 different types. In this paper, we use this dataset to evaluate the possibility of fall detection, more precisely, impact and pre-impact which correspond to fall and pre-fall, respectively. Unlike the classical ML algorithms and threshold-based methods commonly used in previous research works, in this paper, we implement a bidirectional long short-term memory (Bi-LSTM) algorithm which we believe better reflects the impact and pre-impact context as it takes into consideration both backward and forward sequence information at every time step. Our experimental results showed that Bi-LSTM achieves an accuracy of 99.97% and 99.95%, with 99.80% and 99.30% sensitivity, and 100% and 99.99% specificity for fall and pre-fall detections, respectively. These results largely exceed previously published results.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".