Waffle

Abstract

The bathroom has consistently ranked among the most perilous rooms in households, with slip and fall incidents during showers posing a critical threat, particularly to the elders. To address this concern while ensuring privacy and accuracy, the mmWave-based sensing system has emerged as a promising solution. Capable of precisely detecting human activities and promptly triggering alarms in response to critical events, it has proved especially valuable within bathroom environments. However, deploying such a system in bathrooms faces a significant challenge: interference from running water. Similar to the human body, water droplets reflect substantial mmWave signals, presenting a major obstacle to accurate sensing. Through rigorous empirical study, we confirm that the interference caused by running water adheres to a Weibull distribution, offering insight into its behavior. Leveraging this understanding, we propose a customized Constant False Alarm Rate (CFAR) detector, specifically tailored to handle the interference from running water. This innovative detector effectively isolates human-generated signals, thus enabling accurate human detection even in the presence of running water interference. Our implementation of "Waffle" on a commercial off-the-shelf mmWave radar demonstrates exceptional sensing performance. It achieves median errors of 1.8cm and 6.9cm for human height estimation and tracking, respectively, even in the presence of running water. Furthermore, our fall detection system, built upon this technique, achieves remarkable performance (a recall of 97.2% and an accuracy of 97.8%), surpassing the state-of-the-art method.