A WiFi-Based Method for Recognizing Fine-Grained Multiple-Subject Human Activities
Why this work is in the frame
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Bibliographic record
Abstract
Device-free human activity recognition (HAR) has gained attention in recent years. While much has been done in coarse-grained HAR, the recognition of fine-grained human activities is still a research challenge. In this paper, we present a novel method to combine Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) signals at the feature level to improve the performance of device-free fine-grained HAR using WiFi data. We extract 7 CSI and 3 RSSI non-segmented frequency domain features, 12 segmented time-domain features, and 5 segmented frequency-domain features to select the feature set. We evaluate our method using a dataset containing 12 human-to-human fine-grained interactions. We utilized various classification methods like Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Decision Tree (DT), Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Random Forest (RF) using the feature set as input. Our evaluation result yields 94.16% of accuracy, 94.3% of precision, 94.24% of recall, 94.13% f1-score, 93.18% of k-score, and 95.91% AUC in recognition of 7 human-to-human interactions using RF.
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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 | 0.000 |
| 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 it