FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care
Bibliographic record
Abstract
In this paper, we propose a novel system prototype for human activity recognition using a low-cost, low-power millimeter-wave (mmWave) frequency-modulated continuous wave (FMCW) radar. Our approach applies the Fast Fourier Transform on the slow time axis and employs a Capon filter to generate range-Doppler, range-azimuth, and range-elevation maps, respectively. It can also effectively mitigate noise and multipath effects. We then use principal component analysis for feature reduction, reducing the dimensionality of the feature vectors extracted from these maps, which can be used to train conventional machine learning classifiers. This approach aims to achieve a balance between computational complexity, accuracy, and overall system performance. Our proposed system demon-strates promising recognition rates and robustness across varying levels of activity granularity, achieving recognition rates from 90.28% for four activities up to 70.97% for seven fine-grained ac-tivities. These findings highlight the potential of millimeter wave radar and suggested range maps combined with conventional machine learning classifiers for noninvasive, privacy-preserving activity recognition, with significant implications for healthcare, elderly care, and ambient assisted living.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".