Synchro-Squeezed Time-Frequency Representations for Radar-based Human Activity Recognition
Bibliographic record
Abstract
Time-frequency representations such as short-time Fourier transform and wavelet transform of the radar returns capture macro and micro motions of the individuals that facilitate human activity recognition using radars. However, the choice of windows in these transforms constrains the time-frequency resolution and may negatively impact the recognition of human activities. Synchro-squeezed Fourier and synchrosqueezed wavelet transforms are known to provide improved time-frequency resolution and therefore may provide better human activity recognition. This work uses the histogram of gradients (HOG) of the synchro-squeezed transforms as hand-crafted features for radar-based human activity recognition. Two types of synchro-squeezed time-frequency representations, namely, synchro-squeezed Fourier transform (SSFT) and synchro-squeezed wavelet transform (SSWT) are considered in this work. HOG features obtained from the synchrosqueezed transforms, the traditional short-time Fourier transform (STFT), and continuous time wavelet transform (CWT) are used with four well-known machine-learning classifiers, namely stochastic gradient descent (SGD), random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM). A publicly available dataset consisting of radar signatures of human activities recorded at three different locations is used in this study and a location-wise training/testing strategy is utilized. The performance of radar-based activity recognition is significantly improved with the use of synchro-squeezed time-frequency representations as compared to time-frequency representations without synchro-squeezing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".