A new, distinct form of backend processing— a very distant relative of potty training, for computers—is presented in this new study:
“A mountable toilet system for personalized health monitoring via the analysis of excreta,” Seung-min Park, Daeyoun D. Won, Brian J. Lee, Diego Escobedo, Andre Esteva, Amin Aalipour, T. Jessie Ge, Jung Ha Kim, Susie Suh, Elliot H. Choi, Alexander X. Lozano, Chengyang Yao, Sunil Bodapati, Friso B. Achterberg, Jeesu Kim, Hwan Park, Youngjae Choi, Woo Jin Kim, Jung Ho Yu, Alexander M. Bhatt, Jong Kyun Lee, Ryan Spitler, Shan X. Wang, and Sanjiv S. Gambhir, Nature Biomedical Engineering, 2020. (Thanks to Abhishek Nagaraj for bringing this to our attention.)
The authors, at institutions in the USA, South Korea, Canada, and The Netherlands, explain:
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.