research-article

VibroSense: Recognizing Home Activities by Deep Learning Subtle Vibrations on an Interior Surface of a House from a Single Point Using Laser Doppler Vibrometry

Authors:

Benjamin Steeper,

Matthew Dressa,

Cheng ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 3

Article No.: 96, Pages 1 - 28

https://doi.org/10.1145/3411828

Published: 04 September 2020 Publication History

Abstract

Smart homes of the future are envisioned to have the ability to recognize many types of home activities such as running a washing machine, flushing the toilet, and using a microwave. In this paper, we present a new sensing technology, VibroSense, which is able to recognize 18 different types of activities throughout a house by observing structural vibration patterns on a wall or ceiling using a laser Doppler vibrometer. The received vibration data is processed and sent to a deep neural network which is trained to distinguish between 18 activities. We conducted a system evaluation, where we collected data of 18 home activities in 5 different houses for 2 days in each house. The results demonstrated that our system can recognize 18 home activities with an average accuracy of up to 96.6%. After re-setup of the device on the second day, the average recognition accuracy decreased to 89.4%. We also conducted follow-up experiments, where we evaluated VibroSense under various scenarios to simulate real-world conditions. These included simulating online recognition, differentiating between specific stages of a device's activity, and testing the effects of shifting the laser's position during re-setup. Based on these results, we discuss the opportunities and challenges of applying VibroSense in real-world applications.

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Cited By

Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Rui Xiaobo 芮Kong Xinyue 孔Li Leixia 李Wu Zhou 伍Wang Yongbiao 王Wang Yahui 王Xu Lixin 徐Zhang Yu 张Zhang Wenxi 张(2024)激光远距离语音探测技术进展Laser & Optoelectronics Progress10.3788/LOP23052761:5(0500007)Online publication date: 2024
https://doi.org/10.3788/LOP230527
Sheng YLi JMa YZhang J(2024)HomeOSD: Appliance Operating-Status Detection Using mmWave RadarSensors10.3390/s2409291124:9(2911)Online publication date: 2-May-2024
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Index Terms

VibroSense: Recognizing Home Activities by Deep Learning Subtle Vibrations on an Interior Surface of a House from a Single Point Using Laser Doppler Vibrometry
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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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 4, Issue 3

September 2020

1061 pages

EISSN:2474-9567

DOI:10.1145/3422862

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Published: 04 September 2020

Published in IMWUT Volume 4, Issue 3

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https://doi.org/10.1109/OJIM.2024.3502885
Rui Xiaobo 芮Kong Xinyue 孔Li Leixia 李Wu Zhou 伍Wang Yongbiao 王Wang Yahui 王Xu Lixin 徐Zhang Yu 张Zhang Wenxi 张(2024)激光远距离语音探测技术进展Laser & Optoelectronics Progress10.3788/LOP23052761:5(0500007)Online publication date: 2024
https://doi.org/10.3788/LOP230527
Sheng YLi JMa YZhang J(2024)HomeOSD: Appliance Operating-Status Detection Using mmWave RadarSensors10.3390/s2409291124:9(2911)Online publication date: 2-May-2024
https://doi.org/10.3390/s24092911
Wang ZZhang XWang YPeng XXu ZShu YLiu JTan RHe YChen J(2024)Hawk: An Efficient NALM System for Accurate Low-Power Appliance RecognitionProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699359(578-591)Online publication date: 4-Nov-2024
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