<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Song Z</submitter><funding>Agency for Science, Technology and Research</funding><funding>A*STAR</funding><funding>Singapore MOE Tier 1</funding><pagination>e2503247</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12376671</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12(30)</volume><pubmed_abstract>Defecation, a fundamental physiological process, remains underexplored despite its importance in human health. To address this gap, a smart toilet system is developed that enables real-time monitoring of defecation behaviors. Analyzing 45 defecation events from 11 participants, key defecation parameters are identified, including stool dropping duration, stool thickness, and eu-tenesmus interval. Stool dropping duration follows a log-normal distribution, with longer durations (>5 s) linked to lower Bristol Stool Form Scale (BSFS) scores, suggesting constipation (p = 0.008 for BSFS1/2/3 vs BSFS5/6/7). Stool thickness decreases with increasing BSFS scores (p = 5 × 10⁻⁶ for BSFS1/2/3 vs BSFS5/6/7), validating its role as an objective marker for bowel function. Eu-tenesmus is introduced, define</pubmed_abstract><journal>Advanced science (Weinheim, Baden-Wurttemberg, Germany)</journal><pubmed_title>AI-Driven Defecation Analysis by Smart Healthcare Toilet: Exploring Biometric Patterns and Eu-Tenesmus.</pubmed_title><pmcid>PMC12376671</pmcid><funding_grant_id>RS29/23</funding_grant_id><funding_grant_id>M24N9b0119</funding_grant_id><pubmed_authors>Park SM</pubmed_authors><pubmed_authors>Wong SH</pubmed_authors><pubmed_authors>Kwon T</pubmed_authors><pubmed_authors>Rogalla S</pubmed_authors><pubmed_authors>Lee J</pubmed_authors><pubmed_authors>Choi HS</pubmed_authors><pubmed_authors>Jun BH</pubmed_authors><pubmed_authors>Rosen MJ</pubmed_authors><pubmed_authors>Won DD</pubmed_authors><pubmed_authors>Ziyang JK</pubmed_authors><pubmed_authors>Kim S</pubmed_authors><pubmed_authors>Lee BJ</pubmed_authors><pubmed_authors>Park WG</pubmed_authors><pubmed_authors>Hu DL</pubmed_authors><pubmed_authors>Song Z</pubmed_authors><pubmed_authors>Liao JC</pubmed_authors><pubmed_authors>Sonu I</pubmed_authors></additional><is_claimable>false</is_claimable><name>AI-Driven Defecation Analysis by Smart Healthcare Toilet: Exploring Biometric Patterns and Eu-Tenesmus.</name><description>Defecation, a fundamental physiological process, remains underexplored despite its importance in human health. To address this gap, a smart toilet system is developed that enables real-time monitoring of defecation behaviors. Analyzing 45 defecation events from 11 participants, key defecation parameters are identified, including stool dropping duration, stool thickness, and eu-tenesmus interval. Stool dropping duration follows a log-normal distribution, with longer durations (>5 s) linked to lower Bristol Stool Form Scale (BSFS) scores, suggesting constipation (p = 0.008 for BSFS1/2/3 vs BSFS5/6/7). Stool thickness decreases with increasing BSFS scores (p = 5 × 10⁻⁶ for BSFS1/2/3 vs BSFS5/6/7), validating its role as an objective marker for bowel function. Eu-tenesmus is introduced, define</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Aug</publication><modification>2026-05-09T19:08:25.836Z</modification><creation>2026-04-08T01:09:59Z</creation></dates><accession>S-EPMC12376671</accession><cross_references><pubmed>40349171</pubmed><doi>10.1002/advs.202503247</doi></cross_references></HashMap>