Passive indoor visible light-based fall detection using neural networks
Bibliographic record
Abstract
In this paper, a passive visible light sensing (VLS) fall detection system based on luminaires is proposed that uses neural networks to learn the state (i.e., upright or prone) of a target (e.g., a person). The proposed method measures the channel impulse response (CIR) between different source-receiver pairs in a passive scenario, where the user does not hold a device or sensor. The CIR measurements are collected in a realistically modeled room and neural networks are employed to learn the relationship between the CIR measurements and the states of the target at randomly selected positions in the room. The performance evaluation of the system shows that an accuracy of more than 97% is attainable by utilizing a large number of data samples and high brightness factor of the luminaires. The robustness of the proposed method is validated by using a tilted state which is labeled with same class as the upright state, however, the tilted state is not used to train the network. One of the key applications of fall detection is in healthcare domain for patient monitoring. The correct prediction of the prone state is particularly critical in such scenarios since emergency situations may arise from a fall.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".