Non-invasive wearable devices for urinary incontinence detection—a mini review
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
People with urinary incontinence (UI) often face a significant social stigma feeling ashamed of their condition and worrying about others discovering it. In order to improve the quality of life of those with incontinence, recent technological advancements enabled the development of non-invasive devices for detecting urinary leakage (UL). However, no comprehensive study has been conducted to state the most suitable types of sensors and the fundamental features necessary to design such devices, while also pointing gaps for future research. To address this, we conducted a mini review using four electronic databases limiting our search to English-written papers published in peer-reviewed journals. We retrieved articles that met the chosen inclusion criteria and classified them based on sensor type used, its location, the detection technique employed, and whether it was an e-Textile design and a reusable product or not. Across the studies, UL was detected using different approaches leading to heterogeneous results. Electrodes commonly used as sensing elements, along with textile as substrate material, and an indicator of UL based on resistance value, appeared to be widely exploited. However, the outcomes were not correlated with any specific type of UI. Consequently, we hypothesize that any non-invasive device could potentially be used for different types of UI. Nevertheless, further studies need to be conducted to confirm this statement. The designed literature mapping provides readers with an overview of the recent non-invasive wearable technologies in UL detection and offers a roadmap for future innovations.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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