Artificial intelligence of toilet (AI-Toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor

Abstract Smart toilet provides a feasible platform for the long-term analysis of person’s health. Common solutions for identification are based on camera or radio-frequency identification (RFID) technologies, but it is doubted for privacy issues. Here, we demonstrate an artificial intelligence of toilet (AI-toilet) based on a triboelectric pressure sensor array offering a more private approach with low cost and easily deployable software. The pressure sensor array attached on the toilet seat is composed of 10 textile-based triboelectric sensors, which can leverage the different pressure distribution of individual users' seating manner to get the biometric information. 6 users can be correctly identified with more than 90% accuracy using deep learning. The signals from pressure sensors also can be used for recording the seating time on the toilet. The system integrates a camera sensor to analyze the simulated urine by comparing with urine chart and classify the types and quantities of objects using deep learning. All information including two-factor user identification and entire seating time using pressure sensor array, and data from the urinalysis and stool analysis were automatically transferred to a cloud system and were further shown in user's mobile devices for better tracking their health status.