Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case\n Study
Bibliographic record
Abstract
Monitoring indoor activities of daily living (ADLs) of a person is neither an\neasy nor an accurate process. It is subjected to dependency on sensor type,\npower supply stability, and connectivity stability without mentioning artifacts\nintroduced by the person himself. Multiple challenges have to be overcome in\nthis field, such as; monitoring the precise spatial location of the person, and\nestimating vital signs like an individuals average temperature. Privacy is\nanother domain of the problem to be thought of with care. Identifying the\npersons posture without a camera is another challenge. Posture identification\nassists in the persons fall detection. Thermal imaging could be a proper\nsolution for most of the mentioned challenges. It provides monitoring both the\npersons average temperature and spatial location while maintaining privacy. In\nthis research, we propose an IoT system for monitoring an indoor ADL using\nthermal sensor array (TSA). Three classes of ADLs are introduced, which are\ndaily activity, sleeping activity and no-activity respectively. Estimating\nperson average temperature using TSAs is introduced as well in this paper.\nResults have shown that the three activity classes can be identified as well as\nthe persons average temperature during day and night. The persons spatial\nlocation can be determined while his/her privacy is maintained as well.\n
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| 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".