The Development and Testing of Pour-Flush Toilet Sensors for Understanding User Interaction in Peri-Urban Households
Bibliographic record
Abstract
Abstract Rapid worldwide urbanization has created peri-urban environments that often lack services and infrastructure for water and sanitation. Globally, around 4.5 billion people do not have access to safely managed sanitation, as is often the case in such environments. Efforts to develop appropriate sanitation alternatives in these contexts recognize the value of understanding users’ preferences and interaction with their sanitation systems, however, the traditional tools for assessing technology usage and adoption are based on physical observation, which presents limitations. In this work, we developed a toilet sensor to identify usage patterns of pour-flush toilets by quantifying flushing and defecation events. The device has a methane gas sensor, IR distance sensor and a motion sensor connected to a microcontroller. Its small footprint allows for unobtrusive installation inside a toilet bowl and operates battery-powered for about 5 days depending on usage patterns. To evaluate the sensor performance, units were installed for a field trial in nine participants’ households in a Mexican peri-urban community and an algorithm for automated data analysis was developed. Surveys were also conducted to benchmark the sensor performance and determine the potential value of the approach. Results showed that on average people underreported their daily toilet usage by two events compared to the measurements and they flushed only 75% of the time after defecation. By monitoring the usage of the current pour-flush toilets lacking piped water and sewerage and complementing the data with users’ feedback, we can gain an understanding of the existing limitations so more suitable sanitation alternatives can be proposed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".