Technologies for the longitudinal monitoring of a person’s health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user’s excreta through data collection and models of human health. The ‘smart’ toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user’s urine using a standard-of-care colorimetric assay that traces red–green–blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analysed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Restrictions apply to the availability of the medical training and validation data, which were used with permission of the participants for the current study, and are therefore not publicly available. Some of the data may be available from the authors on reasonable request, after permission from the Stanford University School of Medicine and/or the Seoul Song Do Hospital.
The codes may be available from the authors on reasonable request, after permission from the Stanford University School of Medicine and/or Seoul Song Do Hospital.
Porche, D. J. Precision medicine initiative. Am. J. Mens Health 9, 177 (2015).
Ashley, E. A. The precision medicine initiative: a new national effort. JAMA 313, 2119–2120 (2015).
Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).
What is precision medicine? Genetics Home Reference https://ghr.nlm.nih.gov/primer/precisionmedicine/definition (2020).
Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl. Med. 10, eaao3612 (2018).
Blackwell, D. L., Lucas, J. W. & Clarke, T. C. Summary health statistics for U.S. adults: national health interview survey, 2012. Vital Health Stat. 10, 1–161 (2014).
Aalipour, A. et al. Deactivated CRISPR associated protein 9 for minor-allele enrichment in cell-free DNA. Clin. Chem. 64, 307–316 (2018).
Park, S.-m et al. Molecular profiling of single circulating tumor cells from lung cancer patients. Proc. Natl Acad. Sci. USA 113, E8379–E8386 (2016).
Krilaviciute, A. et al. Detection of cancer through exhaled breath: a systematic review. Oncotarget 6, 38643–38657 (2015).
Peng, G. et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103, 542–551 (2010).
Hong, Y. J. et al. Multifunctional wearable system that integrates sweat‐based sensing and vital‐sign monitoring to estimate pre‐/post‐exercise glucose levels. Adv. Funct. Mater. 28, 1805754 (2018).
Bandodkar, A. J. et al. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci. Adv. 5, eaav3294 (2019).
Lee, J.-R., Choi, J., Shultz, T. O. & Wang, S. X. Small molecule detection in saliva facilitates portable tests of marijuana abuse. Anal. Chem. 88, 7457–7461 (2016).
Kim, J. et al. Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 139, 1632–1636 (2014).
Harpole, M., Davis, J. & Espina, V. Current state of the art for enhancing urine biomarker discovery. Expert Rev. Proteom. 13, 609–626 (2016).
Decramer, S. et al. Urine in clinical proteomics. Mol. Cell. Proteom. 7, 1850–1862 (2008).
Davies, R. J., Miller, R. & Coleman, N. Colorectal cancer screening: prospects for molecular stool analysis. Nat. Rev. Cancer 5, 199–209 (2005).
Gisbert, J. P., de la Morena, F. & Abraira, V. Accuracy of monoclonal stool antigen test for the diagnosis of H. pylori infection: a systematic review and meta-analysis. Am. J. Gastroenterol. 101, 1921–1930 (2006).
Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl Acad. Sci. USA 111, 3671–3676 (2014).
Lin, K. Y., Kwong, G. A., Warren, A. D., Wood, D. K. & Bhatia, S. N. Nanoparticles that sense thrombin activity as synthetic urinary biomarkers of thrombosis. ACS Nano 7, 9001–9009 (2013).
Graff, L. A Handbook of Routine Urinalysis (Lippincott, 1983).
Simerville, J. A., Maxted, W. C. & Pahira, J. J. Urinalysis: a comprehensive review. Am. Fam. Physician 71, 1153–1162 (2005).
Schäfer, W. et al. Good urodynamic practices: uroflowmetry, filling cystometry, and pressure‐flow studies. Neurourol. Urodyn. 21, 261–274 (2002).
Dabhoiwala, N., Osawa, D., Lim, A. T. L. & Abrams, P. The ICS-‘BPH’ study: uroflowmetry, lower urinary tract symptoms and bladder outlet obstruction. Br. J. Urol. 82, 619–623 (1998).
Jørgensen, J. B., Jensen, K. E., Bille‐Brahe, N. & Mogensen, P. Uroflowmetry in asymptomatic elderly males. BJU Int. 58, 390–395 (1986).
El Din, K. E., Kiemeney, L., De Wildt, M., Debruyne, F. & de La Rosette, J. Correlation between uroflowmetry, prostate volume, postvoid residue, and lower urinary tract symptoms as measured by the International Prostate Symptom Score. Urology 48, 393–397 (1996).
Markland, A. D. et al. Association of low dietary intake of fiber and liquids with constipation: evidence from the National Health and Nutrition Examination Survey. Am. J. Gastroenterol. 108, 796–803 (2013).
Li, B. R. et al. Risk factors for steatorrhea in chronic pancreatitis: a cohort of 2,153 patients. Sci. Rep. 6, 21381 (2016).
Johnson, C. D. et al. Qualitative assessment of the symptoms and impact of pancreatic exocrine insufficiency (PEI) to inform the development of a patient-reported outcome (PRO) instrument. Patient 10, 615–628 (2017).
Parekh, D. & Natarajan, S. Surgical management of chronic pancreatitis. Indian J. Surg. 77, 453–469 (2015).
Shandro, B. M., Nagarajah, R. & Poullis, A. Challenges in the management of pancreatic exocrine insufficiency. World J. Gastrointest. Pharm. Ther. 9, 39–46 (2018).
de la Iglesia-Garcia, D. et al. Efficacy of pancreatic enzyme replacement therapy in chronic pancreatitis: systematic review and meta-analysis. Gut 66, 1354–1355 (2017).
Park, H. H., Kim, H. Y., Jung, S. E., Lee, S. C. & Park, K. W. Long-term functional outcomes of PPPD in children—nutritional status, pancreatic function, GI function and QOL. J. Pediatr. Surg. 51, 398–402 (2016).
D’haens, G. et al. Fecal calprotectin is a surrogate marker for endoscopic lesions in inflammatory bowel disease. Inflamm. Bowel Dis. 18, 2218–2224 (2012).
Van Rheenen, P. F., Van de Vijver, E. & Fidler, V. Faecal calprotectin for screening of patients with suspected inflammatory bowel disease: diagnostic meta-analysis. Brit. Med. J. 341, c3369 (2010).
Schoepfer, A. M. et al. Fecal calprotectin correlates more closely with the Simple Endoscopic Score for Crohn’s disease (SES-CD) than CRP, blood leukocytes, and the CDAI. Am. J. Gastroenterol. 105, 162–169 (2010).
Imperiale, T. F. et al. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 370, 1287–1297 (2014).
Ahlquist, D. A. et al. Next-generation stool DNA test accurately detects colorectal cancer and large adenomas. Gastroenterology 142, 248–256 (2012).
Lane, M. M., Czyzewski, D. I., Chumpitazi, B. P. & Shulman, R. J. Reliability and validity of a modified Bristol Stool Form Scale for children. J. Pediatr. 159, 437–441 (2011).
Candy, D. & Edwards, D. The management of chronic constipation. Curr. Paediatr. 13, 101–106 (2003).
Halmos, E. P. et al. Inaccuracy of patient-reported descriptions of and satisfaction with bowel actions in irritable bowel syndrome. Neurogastroenterol. Motil. 30, e13187 (2018).
Mínguez, P. M. & Benages, M. A. The Bristol scale—a useful system to assess stool form? Rev. Esp. Enferm. Dig. 101, 305–311 (2009).
Ikenaga, T., Shigematsu, T., Kusumoto, A., Yamamoto, K. & Yada, M. Toilet device with health examination system. US patent US4961431A (1990).
Ikenaga, T., Shigematsu, T., Yada, M., Makita, S. & Kitaura, H. Toilet with urine constituent measuring device. US patent US4962550A (1990).
Nakayama, C. et al. Toilet-bowl-mounted urinalysis unit. US patent US5730149A (1998).
Voswinckel, P. A marvel of colors and ingredients. The story of urine test strip. Kidney Int. Suppl. 47, S3–S7 (1994).
Yang, P. J., Pham, J., Choo, J. & Hu, D. L. Duration of urination does not change with body size. Proc. Natl Acad. Sci. USA 111, 11932–11937 (2014).
Haylen, B. T., Ashby, D., Sutherst, J. R., Frazer, M. I. & West, C. R. Maximum and average urine flow rates in normal male and female populations—the Liverpool nomograms. Br. J. Urol. 64, 30–38 (1989).
Kim, J. H. et al. Terminal dribbling in male patients with lower urinary tract symptoms: relationship with international prostate symptom score and with intravesical prostatic protrusion. BMC Urol. 15, 89 (2015).
Jurman, G., Riccadonna, S. & Furlanello, C. A comparison of MCC and CEN error measures in multi-class prediction. PLoS ONE 7, e41882 (2012).
Gorodkin, J. Comparing two K-category assignments by a K-category correlation coefficient. Comput. Biol. Chem. 28, 367–374 (2004).
Wei, J.-M., Yuan, X.-J., Hu, Q.-H. & Wang, S.-Q. A novel measure for evaluating classifiers. Expert Syst. Appl. 37, 3799–3809 (2010).
Blake, M., Raker, J. & Whelan, K. Validity and reliability of the Bristol stool form scale in healthy adults and patients with diarrhoea-predominant irritable bowel syndrome. Aliment. Pharmacol. Ther. 44, 693–703 (2016).
Krishnasamy, P., Belongie, S. & Kriegman, D. Wet fingerprint recognition: challenges and opportunities. In Proc. 2011 International Joint Conference on Biometrics 1–7 (IEEE, 2011).
Gonzalez, R. C. & Woods, R. E. Digital Image Processing (Addison-Wesley, 1992).
Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).
Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
von Münch, E. & Milosevic, D. Qualitative Survey on Squatting Toilets and Anal Cleansing with Water with a Special Emphasis on Muslim and Buddhist Countries by Using the SuSanA Discussion Forum (Ostella Consulting, 2015).
Krhut, J. et al. Comparison between uroflowmetry and sonouroflowmetry in recording of urinary flow in healthy men. Int J. Urol. 22, 761–765 (2015).
Yang, P. J., LaMarca, M., Kaminski, C., Chu, D. I. & Hu, D. L. Hydrodynamics of defecation. Soft Matter 13, 4960–4970 (2017).
Van Rossum, L. G. et al. Random comparison of guaiac and immunochemical fecal occult blood tests for colorectal cancer in a screening population. Gastroenterology 135, 82–90 (2008).
Morikawa, T. et al. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population. Gastroenterology 129, 422–428 (2005).
Guittet, L. et al. Comparison of a guaiac based and an immunochemical faecal occult blood test in screening for colorectal cancer in a general average risk population. Gut 56, 210–214 (2007).
Raman, M. et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 11, 868–875 (2013).
Damman, C. J., Miller, S. I., Surawicz, C. M. & Zisman, T. L. The microbiome and inflammatory bowel disease: is there a therapeutic role for fecal microbiota transplantation? Am. J. Gastroenterol. 107, 1452–1459 (2012).
Ley, R. E. Obesity and the human microbiome. Curr. Opin. Gastroenterol. 26, 5–11 (2010).
Chang, J. Y. et al. Decreased diversity of the fecal microbiome in recurrent Clostridium difficile—associated diarrhea. J. Infect. Dis. 197, 435–438 (2008).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proc. 25th International Conference on Neural Information Processing Systems — Volume 1 1097–1105 (Curran Associates, 2012).
Abadi, M. et al. Tensorflow: a system for large-scale machine learning. In Proc. 12th USENIX Symposium on Operating Systems Design and Implementation 265–283 (USENIX, 2016).
We thank N. Baneai for insight and discussion regarding stool analysis; K. J. Chung for discussion on uroflowmetry implementation; P. Yock for discussion on overall project guidance and on managing intellectual property; C. Kim and I. Steinberg for helpful guidance on image reconstruction; C. T. Chan for the scientific proofreading of the manuscript; D. Chon (Albany High School, CA, USA) for her contribution to 3D CAD designs, and the real-time image analysis for the urinalysis module; the Stanford Clinical Laboratory for overviewing their urinalysis equipment; and staff at the Stanford Product Realization Laboratory, SPF, Stanford Byers Center for Biodesign, Stanford Urology Clinic and S. Taheri for their services. D.E. and S.-m.P. acknowledge the support of the Stanford Institutes of Medicine Summer Research Program. This work was supported in part by the Canary Foundation (to S.S.G.) and in part by NIH/NCI Training Grant T32 CA118681. The REDCap platform services are made possible by Stanford School of Medicine Research Office. The REDCap platform services at Stanford are subsidized by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085. The data content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
S.-m.P., D.D.W., B.J.L., S.B., D.E. and S.S.G. are co-inventors of a patent application filed by Stanford University on the subject of this work (US patent application number 62/695326). S.S.G. is a consultant or receives funding from several companies that work in the healthcare space although none of these companies are directly involved in the current work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Park, Sm., Won, D.D., Lee, B.J. et al. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat Biomed Eng 4, 624–635 (2020). https://doi.org/10.1038/s41551-020-0534-9
Stool pattern is associated with not only the prevalence of tumorigenic bacteria isolated from fecal matter but also plasma and fecal fatty acids in healthy Japanese adults
BMC Microbiology (2021)
Nature Biomedical Engineering (2021)
Nature Reviews Gastroenterology & Hepatology (2021)
Nature Reviews Gastroenterology & Hepatology (2021)
Design and evaluation of a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes
Scientific Reports (2021)