Wireless Multifrequency Feature Set to Simplify Human 3-D Pose Estimation
Bibliographic record
Abstract
We present a multifrequency feature set to detect driver's three-dimensional (3-D) head and torso movements from fluctuations in the radio frequency channel due to body movements. Current features used for movement detection are based on the time-of-flight, received signal strength, and channel state information and come with the limitations of coarse tracking, sensitivity toward multipath effects, and handling corrupted phase data, respectively. There is no standalone feature set that accurately detects small and large movements and determines the direction in 3-D space. We resolve this problem by using two radio signals at widely separated frequencies in a monostatic configuration. By combining information about displacement, velocity, and direction of movements derived from the Doppler effect at each frequency, we expand the number of existing features. We separate pitch, roll, and yaw movements of head from torso and arm. The extracted feature set is used to train a K-Nearest Neighbor classification algorithm, which could provide behavioral awareness to cars while being less invasive as compared to camera-based systems. The training results on data from four participants reveal that the classification accuracy is 77.4% at 1.8 GHz, it is 87.4% at 30 GHz, and multifrequency feature set improves the accuracy to 92%.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".