Song Zhiquan, Kwon TaeHyung, Lee Jeung, Won Daeyoun D, Lee Brian J, Choi Hyuk Soon, Liao Joseph C, Park Walter G, Sonu Irene, Rogalla Stephan, Rosen Michael J, Hu David L, Ziyang Jonathan Kuang, Wong Sunny Hei, Jun Bong Hyun, Kim Soh, Park Seung-Min
School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA.
Adv Sci (Weinh). 2025 Aug;12(30):e2503247. doi: 10.1002/advs.202503247. Epub 2025 May 11.
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.
排便作为一项基本的生理过程,尽管对人类健康至关重要,但仍未得到充分研究。为了填补这一空白,研发了一种智能马桶系统,能够实时监测排便行为。通过分析11名参与者的45次排便事件,确定了关键的排便参数,包括粪便掉落持续时间、粪便厚度和排便后不适间隔时间。粪便掉落持续时间呈对数正态分布,较长的持续时间(>5秒)与较低的布里斯托大便分类法(BSFS)评分相关,提示便秘(BSFS 1/2/3与BSFS 5/6/7相比,p = 0.008)。粪便厚度随BSFS评分增加而减小(BSFS 1/2/3与BSFS 5/6/7相比,p = 5×10⁻⁶),证实了其作为肠道功能客观标志物的作用。引入了排便后不适的概念,定义为最后一次粪便掉落与清洁之间的间隔时间,平均为74.8秒。它显示出显著的性别差异(p = 0.014),但与粪便稠度无关,表明其作为肠道健康独立生物标志物的潜力。还详细比较了人与动物之间的排便行为。纵向监测证明了个性化健康跟踪和饮食建议的潜力。此外,利用包括粪便特性和清洁行为在内的11个与排便相关的参数确定了生物识别的可行性。这些特征能够高度区分参与者,支持非侵入式身份验证。
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