InARMS: Individual Activity Recognition of Multiple Subjects with FMCW radar
Bibliographic record
Abstract
Human Activity Recognition (HAR) can be useful in various applications such as health monitoring, security and surveillance, and smart environments. But the majority of existing HAR methods fail to recognize more than one subject in the environment. Moreover, usually a machine learning algorithm is applied for recognition which needs access to a suitable training dataset and the necessary processing power. In this paper, a non-learning approach for recognizing human activities in multi-subject environments is proposed. For this purpose, microwave Frequency-Modulated Continuous Wave (FMCW) radar is used which is able to work unobtrusively and also does not need any adjustments in different environments. We propose mathematical and morphological operations of range-Doppler map to enable the system to recognize activities in real time with inexpensive and low-power processors. Our system also measures the distance of subjects, in addition to their activity. Performance results show that InARMS can reach 89.1% and 75.1% accuracy in an environment with one and two subjects, respectively, outperforming representative existing methods by as much as 6.4%.
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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".