Artificial intelligence of toilet (AI-Toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Zixuan; Shi, Qiongfeng; He, Tianyiyi; Guo, Xinge; Dong, Bowei; Lee, Jason; Lee, Chengkuo
• Is Part Of: Nano energy. Volume 90(2021)Part A
• Publication Date: 2021-12
• Subjects: Smart toilet -- Triboelectric -- Pressure sensor -- Deep learning -- AI-toilet; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2021.106517
• Languages: English
• ISSNs: 2211-2855

Abstract

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. Graphical Abstract: ga1 Highlights: AI-toilet based on a triboelectric sensor for biometrics identification and a image sensor for urinalysis and stool analysis. Frustum structure and spacer structure on eco-flex layer extend the sensing range for detection of seating pressure. The biometrics information from 6 users seating on the toilet seat can be identified with the accuracy of 97.14%. Two CNNs get the accuracy of 97.50% and 91.15% for simulated 4 different types of stools and stools' amounts.