A new study funded by the United States National Institutes of Health documents how a disease-detecting “precision health” toilet can sense multiple signs of illness through automated urine and stool analysis. The toilet has numerous technologies that use motion sensing to launch tests such as urine samples and stool assessment. The toilet automatically sends data to a cloud-based system. The toilet can measure 10 different biomarkers via urine analysis. The smart toilet can detect abnormal urine flow rate, stream time, and volume. A motion sensor activates the smart toilet to analyze video. The analytics can analyze fecal matter from the images and classifies it according to the Bristol stool chart.
The toilet It has a built-in identification system that uses a flush level which reads fingerprints. “The whole point is to provide precise, individualized health feedback, so we needed to make sure the toilet could discern between users,” Gambhir said to Stanford Medicine. “To do so, we made a flush lever that reads fingerprints.” The team realized, however, that fingerprints foolproof. So just in case there are two people in the bathroom and someone uses the toilet and another flushes it, a camera can identify the person with a picture of the back of the person- what the article calls “the distinctive features of their anoderm [skin tissue lining of the anus].”
Devised by doctors in the Bristol Royal Infirmary, England, and based on the bowel movements of nearly 2,000 people, the Bristol stool chart characterizes the different types of poop as shown above.
A paper describing the research entitled A mountable toilet system for personalized health monitoring via the analysis of excreta was published April 6 in Nature Biomedical Engineering and can be found here.
Stanford Medicine ‘Smart toilet’ monitors for signs of disease”
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
Link to study online https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377213/
