research-article

Waffle: A Waterproof mmWave-based Human Sensing System inside Bathrooms with Running Water

Authors:

Daqing ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 7, Issue 4

Article No.: 201, Pages 1 - 29

https://doi.org/10.1145/3631458

Published: 12 January 2024 Publication History

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.

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Xu ZZhang JGreenberg JFrumkin MJaveed SZhang JBenedict BBotterbush KRodebaugh TRay WLu C(2024)Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596288:2(1-30)Online publication date: 15-May-2024
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Jeong DHan K(2024)PRECYSE: Predicting Cybersickness using Transformer for Multimodal Time-Series Sensor DataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595948:2(1-24)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659594
Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
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Index Terms

Waffle: A Waterproof mmWave-based Human Sensing System inside Bathrooms with Running Water
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7, Issue 4

December 2023

1613 pages

EISSN:2474-9567

DOI:10.1145/3640795

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Copyright © 2024 ACM.

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Published: 12 January 2024

Published in IMWUT Volume 7, Issue 4

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Jeong DHan K(2024)PRECYSE: Predicting Cybersickness using Transformer for Multimodal Time-Series Sensor DataProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595948:2(1-24)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659594
Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Chang ZZhang FXiong JChen WZhang DGanesan DLane NShi W(2024)MSense: Boosting Wireless Sensing Capability Under Motion InterferenceProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649350(108-123)Online publication date: 29-May-2024
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Zhang RFlathmann CMusick GSchelble BMcNeese NKnijnenburg BDuan W(2024)I Know This Looks Bad, But I Can Explain: Understanding When AI Should Explain Actions In Human-AI TeamsACM Transactions on Interactive Intelligent Systems10.1145/363547414:1(1-23)Online publication date: 5-Feb-2024
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Wang ZGuo YRen ZSong WSun ZChen CGuo BYu Z(2024)LiqDetectorProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314437:4(1-24)Online publication date: 12-Jan-2024
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Zhang DZhang XXie YZhang FWang XLi YZhang D(2024)LoCalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314367:4(1-27)Online publication date: 12-Jan-2024
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