Human Activity and Posture Classification Using Single Non-Contact Radar Sensor
Bibliographic record
Abstract
Radar has been proposed for monitoring the health of elderly patients in long term care because it is safe, non-contact and preserves the privacy of patients.Random body movements (RBM) obscure radar return signals making it difficult if not impossible to accurately estimate vitals.Activity classification is presented in this thesis as a preprocessing step for dealing with RBMs.Posture classification is presented in this thesis for assistance in preventing falls.Two popular radar architectures-continuous wave (CW) Doppler and ultra-wideband (UWB) are investigated in this thesis.Activity classification is performed with 92% average accuracy with CW and 86% with UWB.Posture Classification is performed with 64% average accuracy with CW and 85% with UWB.An occupancy detection algorithm was also developed for UWB and achieved 88% average accuracy.The contribution of this thesis is a proposed hierarchical processing approach for both radar types capable of dealing with moving subjects.
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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 | 0.000 |
| 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) | 0.001 | 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".