Enhancing Generalization in Human Activity Recognition Through Improved WI-FI Channel State Information Phase Processing and Antenna Pair Selection
Bibliographic record
Abstract
Utilizing Wi-Fi signals to recognize human activities indoors has become increasingly popular for interfacing with smart devices. However, the lack of acceptable generalization in new conditions and for new users has made Wi-Fi-based activity recognition impractical in daily usage. Although previous studies have attempted to introduce various methods for HAR using Wi-Fi channel state information (CSI), to the best of our knowledge, these methods are reliable only under highly controlled, constant experimental conditions. This work focuses on improving the generalization of HAR for unseen users and proposes a novel method for CSI-based HAR, specifically designed for the CSI collected using Wi-Fi routers with multiple antennas. This process involves selecting the best pair of antennas that can effectively eliminate most of the common noise present in the antennas and training a separate classifier for each antenna pair. The results demonstrate that this method significantly enhances the accuracy of Wi-Fi-based HAR on previously unseen users.
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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 | 0.000 | 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 | 0.003 |
| 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 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".