On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach
Bibliographic record
Abstract
The deeply penetrated WiFi signals not only provide fundamental communications for the massive Internet of Things devices but also enable cognitive sensing ability in many other applications, such as human activity recognition. State-of-the-art WiFi-based device-free systems leverage the correlations between signal changes and body movements for human activity recognition. They have demonstrated reasonably good recognition results with a properly placed transceiver pair, or, in other words, when the human body is within a certain sweet zone. Unfortunately, the sweet zone is not ubiquitous. When the person moves out of the area and enters a dead zone, or even just the orientation changes, the recognition accuracy can quickly decay. In this paper, we closely examine such spatial diversity in WiFi-based human activity recognition. We identify the dead zones and their key influential factors, and accordingly present WiSDAR, a WiFi-based spatial diversity-aware device-free activity recognition system. WiSDAR overshadows the dead zones yet with only one physical WiFi sender and receiver. The key innovation is extending the multiple antennas of modern WiFi devices to construct multiple separated antenna pairs for activity observing. Profiling activity features from multiple spatial dimensions can be more complicated and offer much richer information for further recognition. To this end, we propose a deep learning-based framework that integrates the hidden features from both temporal and spatial dimensions, achieving highly accurate and reliable recognition results. WiSDAR is fully compatible with commercial off-the-shelf WiFi devices, and we have implemented it on the commonly available Intel WiFi 5300 cards. Our real-world experiments demonstrate that it recognizes human activities with a stable accuracy of around 96%.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".