LiqDetector

Wang, Zhu; Guo, Yifan; Ren, Zhihui; Song, Wenchao; Sun, Zhuo; Chen, Chao; Guo, Bin; Yu, Zhiwen

doi:10.1145/3631443

Cited by 4 publications

References 39 publications

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LoCal

Zhang,

Xie

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Millimeter wave (mmWave) radar excels in accurately estimating the distance, speed, and angle of the signal reflectors relative to the radar. However, for diverse sensing applications reliant on radar's tracking capability, these estimates must be transformed from radar to room coordinates. This transformation hinges on the mmWave radar's location attribute, encompassing its position and orientation in room coordinates. Traditional outdoor calibration solutions for autonomous driving utilize corner reflectors as static reference points to derive the location attribute. When deployed in the indoor environment, it is challenging, even for the mmWave radar with GHz bandwidth and a large antenna array, to separate the static reference points from other multipath reflectors. To tackle the static multipath, we propose to deploy a moving reference point (a moving robot) to fully harness the velocity resolution of mmWave radar. Specifically, we select a SLAM-capable robot to accurately obtain its locations under room coordinates during motion, without requiring human intervention. Accurately pairing the locations of the robot under two coordinate systems requires tight synchronization between the mmWave radar and the robot. We therefore propose a novel trajectory correspondence based calibration algorithm that takes the estimated trajectories of two systems as input, decoupling the operations of two systems to the maximum. Extensive experimental results demonstrate that the proposed calibration solution exhibits very high accuracy (1.74 cm and 0.43° accuracy for location and orientation respectively) and could ensure outstanding performance in three representative applications: fall detection, point cloud fusion, and long-distance human tracking.

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LoCal

Zhang,

Xie

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Waffle

Zhang,

Xie

et al. 2023

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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