Human Fall Detection using Multimodal Dataset
Bibliographic record
Abstract
As the global population ages, falls among older individuals are becoming increasingly common and pose significant health risks. The consequences of a fall can be severe, particularly if the individual does not receive timely medical intervention, potentially resulting in serious injuries or fatalities. Developing reliable fall detection systems is crucial in reducing response times and mitigating the health risks. Multimodal fall detection, which consider various types of data such as motion and visual footprints, can provide a comprehensive representation of fall incidents. This research focuses on evaluating the performance of different machine learning models in detecting falls using the multimodal datasets, namely data from accelerometers and RGB and depth cameras. Specifically, we investigate the efficacy of three types of models: Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and a hybrid model that combines CNNs and LSTMs (CNN-LSTM). The LSTM model is known for capturing temporal dependencies in sequential data, the CNN model for extracting spatial features from images, and the hybrid CNN-LSTM model for combining the strengths of both approaches. The experimental results suggest that the hybrid model outperforms the CNN and LSTM separately.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".