Journal Article Ppublish 3 citations
PubMed: 40349171 DOI: 10.1002/advs.202503247
AI-Driven Defecation Analysis by Smart Healthcare Toilet: Exploring Biometric Patterns and Eu-Tenesmus.
Adv Sci (Weinh) 12(30):e2503247
Authors: Zhiquan Song, TaeHyung Kwon, Jeung Lee, Daeyoun D Won, Brian J Lee, Hyuk Soon Choi, Joseph C Liao, Walter G Park, Irene Sonu, Stephan Rogalla, Michael J Rosen, David L Hu, Jonathan Kuang Ziyang, Sunny Hei Wong, Bong Hyun Jun, Soh Kim, Seung-Min Park
Affiliations
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA.
- Seokjeong Wellpark Hospital, Jeollabuk-do, Republic of Korea. Kanaria Health, Seoul, Republic of Korea.
- School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea.
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Gastroenterology & Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Gastroenterology & Hepatology, Tan Tock Seng Hospital, Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea.
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, defined as the interval between the last stool drop and cleansing, averaging 74.8 s. It shows significant gender differences (p = 0.014) but no correlation with stool consistency, suggesting its potential as an independent biomarker for gut health. Defecation behaviors between humans and animals is also compared in detail. Longitudinal monitoring demonstrates the potential for personalized health tracking and dietary recommendations. Furthermore, the feasibility of biometric identification is established using 11 defecation-related parameters, including stool properties and cleansing behavior. These features enable high participant differentiation, supporting non-invasive identity verification.
MeSH
Biomedical Research, Biometry, Defecation, Artificial Intelligence, Digital Biomarkers