Highly Accurate Bathroom Activity Recognition Using Infrared Proximity Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among elderly populations over the world, a high percentage of individuals are affected by physical or mental diseases, greatly influencing their quality of life. As it is a known fact that they wish to remain in their own home for as long as possible, solutions must be designed to detect these diseases automatically, limiting the reliance on human resources. To this end, our team developed a sensors platform based on infrared proximity sensors to accurately recognize basic bathroom activities such as going to the toilet and showering. This article is based on the body of scientific literature which establish evidences that activities relative to corporal hygiene are strongly correlated to health status and can be important signs of the development of eventual disorders. The system is built to be simple, affordable and highly reliable. Our experiments have shown that it can yield an F-Score of 96.94%. Also, the durations collected by our kit are approximately 6 seconds apart from the real ones; those results confirm the reliability of our kit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.003 |
| 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 it