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.

智能马桶为长期分析人的健康状况提供了一个可行的平台。常见的识别解决方案是基于摄像头或射频识别 (RFID) 技术，但存在隐私问题。在这里，我们展示了一种基于摩擦电压力传感器阵列的人工智能厕所（AI-toilet），提供了一种成本更低且易于部署的软件的更私密的方法。附着在马桶座圈上的压力传感器阵列由10个基于织物的摩擦电传感器组成，可以利用个体用户坐姿的不同压力分布来获取生物特征信息。使用深度学习可以以超过 90% 的准确率正确识别 6 个用户。来自压力传感器的信号还可用于记录在马桶上的就座时间。该系统集成了一个摄像头传感器，通过与尿图比较来分析模拟尿液，并使用深度学习对物体的类型和数量进行分类。所有信息，包括使用压力传感器阵列的双因素用户识别和整个就座时间，以及来自尿液分析和粪便分析的数据，都自动传输到云系统，并进一步显示在用户的移动设备中，以便更好地跟踪他们的健康状况。