Human Activity Recognition Using WiFi Signal Features and Efficient Residual Packet Attention Network
Bibliographic record
Abstract
WiFi signal features, particularly channel state information (CSI), have gained considerable attention in human activity recognition (HAR) due to their nonintrusive and privacy–friendly nature. However, CSI packets are often nonstationary and exhibit fluctuations across various human activities. In this letter, we propose an end-to-end deep neural network (DNN) called efficient residual packet attention network (ERPANet) to tackle these challenges. In the proposed framework, we introduce the multilayer residual module composed of an attention residual (AR) operation and a downsampling attention residual (DAR) operation to effectively capture spatial-temporal features of CSI packets. In addition, a self-attention mechanism is embedded within AR and DAR to emphasize the importance of interrelationship among these multiscale CSI packet features. The proposed ERPANet aims to encode both channel information and long-range dependencies of CSI packet features. Extensive experiments show that ERPANet outperforms state-of-the-art methods, achieving average accuracies of 99.4% and 99.6% on the university of toronto human activity recognition (UT-HAR) and nanyang technological university human activity recognition (NTU-HAR) datasets, respectively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".