Comparison of Multimodal Fall Detection Strategies
Bibliographic record
Abstract
Falls represent a major health concern for older adults, underscoring the need for accurate and timely detection systems. Conventional wearable or observer-based methods often suffer from compliance issues, discomfort, and high false alarm rates. This work investigates multimodal fall detection by combining motion sensors, triaxial accelerometers, and RGB-D data to capture both human movement and environmental context. We evaluate deep learning architectures, focusing on CNN-LSTM models with two fusion strategies: early fusion, which integrates multimodal features before temporal modeling, and intermediate fusion, which combines modality-specific representations at a later stage. Experiments conducted on a publicly available dataset of fall and non-fall activities demonstrate that the CNN-LSTM intermediate fusion model achieves higher accuracy, precision, and recall compared to early fusion and unimodal baselines. The results highlight the robustness of multimodal deep learning with intermediate fusion, offering a promising direction for reliable real-time fall detection in smart home and eldercare environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".