Light-Weight Learning Model with Patch Embeddings for Radar-based Fall Event Classification: A Multi-domain Decision Fusion Approach
Bibliographic record
Abstract
With the increasing morbidity and mortality rate in older adults above 65 years of age due to accidental fall, privacy-preserving radar-based fall event detection is becoming crucial. Deep learning algorithm like vision transformers (ViT) for human fall-event detection using different radar domain representation have shown excellent fall-detection accuracy. However, such techniques are computationally very expensive and unsuitable when training datasets are small. Patch-based learning models such as Multi-Layer Perceptron-Mixer (MLP-Mixer) and Convolutional-Mixer (ConvMixer) models have been developed as alternatives to ViT. In this work, the decision outputs of light-weight ConvMixer models with different domain representations of radar returns as inputs are fused for classifying the events as fall or non-fall. This proposed approach of event classification utilizes supplementary information present in different domains for enhancing the classification accuracy. Evaluation done on publicly available dataset shows an improved performance of the multi-domain ConvMixer model over ViT and MLP-Mixer. This further justifies the choice of light weight ConvMixer as a preferred learnable model when only limited training dataset is available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".