DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
WiFi-based Human Activity Recognition (HAR) enables privacy-preserving, device-free motion detection using Channel State Information (CSI) from commodity devices. However, CSI's low resolution and noise complicate spatiotemporal feature extraction. We propose DSA-Net, a Dual-Path Spatial-Temporal Attention Network tailored to CSI-based HAR. It combines a slow-fast temporal pathway with cross-spatial attention to capture fine-grained and long-range dependencies, while a Transformer-based fusion module adaptively integrates spatiotemporal features. Evaluated on the Widar3.0 dataset, DSA-Net surpasses Vision Transformer (ViT) baselines by 18.91 percentage points, achieving superior accuracy with low computational overhead. Our results demonstrate DSA-Net’s potential for scalable, real-time activity recognition in IoT and smart environments.
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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 | 0.000 |
| 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 it