DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".