Solomon Cheung¹, Yu Sun¹ and Fangyan Zhang², ¹California State Polytechnic University, USA and ²ASML, USA

As an act of disposing waste and maintaining homeostasis, humans have to use the restroom multiple times a day. One item that is consumed in the process is toilet paper; it often runs out easily in the most inconvenient times. One of the most fatal positions to be in is to be stuck without toilet paper. Since humans are not capable of a 100% resupply rate, we should give this task to a computer. The approach we selected was to use a pair of laser sensors to detect whether toilet paper was absent or not. Utilizing an ultrasound sensor, we would be able to detect whether a person was nearby and send a notification to a database. The online app, PaperSafe, takes the information stored and displays it onto a device for quick access. Once a sufficient amount of data is acquired, we can train a machine learning algorithm to predict the next supply date, optimized for the specific scenario

Amenity, Homeostasis, Machine Learning, Mobile Application