Parametric Convolutional Neural Network for Radar-based Human Activity Classification Using Raw ADC Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Radar sensors offer a promising and effective sensing modality for human activity classification. Human activity classification enables several smart homes applications for energy saving, human-machine interface for gesture controlled appliances and elderly fall-motion recognition. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or video of range-Doppler images (RDIs), followed by deep neural network or machine learning for classification. Although, deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as camera and speech, yet for the case of radar DCNN preprocessing followed by feature image generation, such as video of RDI or Doppler spectrogram, is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar preprocessing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. It is demonstrated that our proposed solution shows improved results compared to equivalent state-of-art DCNN solutions that rely on Doppler spectrogram or video of RDIs as feature images.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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