Multi-Adversarial In-Car Activity Recognition Using RFIDs
Bibliographic record
Abstract
In-car human activity recognition opens a new opportunity toward intelligent driving behavior detection and touchless human-car interaction. Among the many sensing technologies (e.g., using cameras and wearable sensors), radio frequency identification (RFID) exhibits unique advantages given its low cost, easy deployment, and less privacy concerns. Existing RFID-based solutions for activity recognition are mostly confined to working in stable indoor spaces. The inside space of a car however is much more compact and complex, not to mention the fast-changing driving conditions. All these introduce non-negligible noises that pollute the activity-related information, and the existence of various car models in the market further complicates the problem. In this article, we for the first time closely examine the distinct factors that affect the RFID-based in-car activity recognition. We present RF-CAR, a novel RFID-based tag-free solution that well adapts to different in-car environments. RF-CAR smartly filters the domain-specific features in RF signals and retains activity-related features to the maximum extent. It then integrates a deep learning architecture and an advanced multi-adversarial domain adaptation network for training and prediction. With only one-time pre-training, RF-CAR can adapt to new data domains such as new driving conditions, car models, and human subjects for robust activity recognition. We also demonstrate that it is readily deployable in cars with commercial off-the-shelf (COTS) RFID devices. Our extensive experiments suggest that RF-CAR achieves an overall recognition accuracy of around 95 percent, which significantly outperforms the state-of-the-art solutions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".