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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".