Dual-Path Model With Fresnel Zone-Based Voting For Human Activity Recognition Using WI-FI
Bibliographic record
Abstract
In Wi-Fi sensing, the spatial diversity granted by using multiple access points (AP) can significantly improve the performance of human activity recognition (HAR), due to the importance of the user location and orientation. Existing approaches leverage this fact by assuming channel state information (CSI) data is transmitted from the different APs to a central server for further processing. This approach is impractical due to the communication overhead of constantly transmitting CSI. In this paper, we propose a distributed model architecture, where dual-path light-weight models run on each AP, and the two paths are used for feature extraction along the time and frequency axes, respectively. When gestures take place, model outputs are combined centrally using a Fresnel zone-based voting scheme, making efficient use of spatial diversity to improve performance, while eliminating the costly CSI transmissions. Experiments on two datasets demonstrate the practicality and high accuracy of our approach for HAR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".