Phenotype Matching: RF Sensor-Based Indoor Subject Identification with Wearable Sensor Assistance
Bibliographic record
Abstract
In this paper, we propose a solution to a major challenge in RF sensor-based indoor monitoring-subject identification-by integrating an additional wearable sensor. Compared to existing approaches that rely on machine learning, which require user-specific data collection and training for new subjects or environments, our method is customization-free. It leverages the fact that both RF sensors and wearable devices can collect and extract comparable physiological time-series signals, including body movements and vital signs, which we collectively refer to as phenotypes. This enables matching based on signal similarity, allowing subject identities to be determined through the identity information embedded in the wearable sensors. We demonstrate the feasibility of phenotype matching between RF sensors and common wearable devices, including wristband-based photoplethysmography (PPG) sensors, accelerometers, chest-worn respiratory belts, and pedometers. Our system supports scenarios where subjects are either stationary or in motion, enabling accurate real-time identity resolution. By bridging the strengths of contactless radar sensing and user-attached wearables, our approach supports scalable, long-term behavioral monitoring in indoor, multi-subject environments without compromising privacy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".