Comparison of Fundamental Radar Features for Differentiating Between Walking and Standing in Horizontal and Vertical Movement Directions
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates the feasibility of using a basic 24 GHz one-dimensional (1D) radar for Human Activity Recognition (HAR), focusing on differentiating between walking and standing movements.We evaluate the radar's performance using various machine learning models, including K-means, GMM, SVM, and LSTM.Using the silhouette score and the Davies-Bouldin index, we evaluate the intra-and interclass results of K-means and GMM, while SVM and LSTM are used to analyze their performance.The results indicate that the LSTM model achieves high accuracy in both vertical and horizontal dimensions, with precision, recall, and F1-scores all above 98% for both standing and walking movements.However, the SVM model faces challenges in horizontal movement detection, consistent with the unsupervised learning results where the inter-class and intra-class distances for the horizontal dimension are not significant, making differentiation difficult.These findings delineate the boundaries and capabilities of a lower-specification radar for HAR, providing insights into its practical applications and limitations.
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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