Fall Detection in Ambient-Assisted Living Environments Using FMCW Radars and Deep Learning
Bibliographic record
Abstract
The global rise in the elderly population has increased the demand for effective fall detection in Ambient Assisted Living (AAL) environments. This paper introduces a novel and reliable fall detection system utilizing frequencymodulated continuous wave (FMCW) radar, designed to address privacy concerns, operate reliably in low-light conditions, and provide ease of installation. Data from two wall-mounted radars capture a variety of activities, including simulated falls, across five configurations to enhance model generalizability. Radar data processing employs the Fast Fourier Transform (FFT) and the Capon algorithm to generate Range-Azimuth and Range-Elevation maps, which serve as input features for a proposed 3D Convolutional Neural Network (3D CNN) model. This model achieves an accuracy of 94.33 % and F1-score of 93.5 %, combining high performance with adaptability across diverse environments and user needs. This work provides a robust solution for fall detection with significant potential for deployment in real-world elderly care settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".