A Haar Wavelet Down-Sampling Linear Deformable ConvFormer for Radar-Based Human Indoor Activity Recognition
Bibliographic record
Abstract
In the context of modern smart home and healthcare automation, accurately monitoring and identifying human activities indoors is crucial. In this paper, we developed the Haar wavelet down-sampling linear deformable ConvFormer (HWDLD-ConvFormer), a novel model specifically designed for human activity recognition (HAR). The model integrates Haar wavelet downsampling (HWD) with linear deformable convolution (LDConv) within a ConvFormer architecture, enabling efficient extraction and processing of complex radar data. Through extensive experiments, HWDLD-ConvFormer demonstrated an average accuracy improvement of 6.16% over the base ConvFormer model across various HAR events, achieving significant performance gains in Precision, Recall, and F1-Score. Moreover, when compared to other state-of-the-art algorithms, including VIT, VIT+CNN, and 2D-Transformer, HWDLD-ConvFormer outperformed all, showing an 8.3% improvement in accuracy and an 8.9% enhancement in recall over the best alternative.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".