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Detecting State Changes of Indoor Everyday Objects using Wi-Fi Channel State Information

Published: 11 September 2017 Publication History

Abstract

Detecting the events of indoor everyday objects such as door or window open/close events has been actively studied to implement such applications as intrusion detection, adaptive HVAC control, and monitoring an independently living elderly person. This study proposes a method for detecting the events and states of indoor everyday objects such as doors and windows without using distributed sensors attached to the objects. In this study, we achieve practical and unobtrusive event detection using a commodity Wi-Fi access point and a computer equipped with a commodity Wi-Fi module. Specifically, we detect the events using Wi-Fi channel state information (CSI), which describes how a signal propagates from a transmitter to a receiver, and is affected by such events. To handle CSI data that consists of the mixed effects of multiple indoor objects in an environment of interest, we employ independent component analysis to separate the events caused by the objects. The decomposed data are then fed into our event classifier based on convolutional and recurrent neural networks to automatically extract features from CSI data, as it is difficult to intuitively design features to be extracted from the CSI data. Moreover, we correct the neural network estimates by incorporating knowledge about the state transitions of an object using hidden Markov models. For example, because the “open” event of a door occurs only when the door is in a “closed” state. We correct impossible state transitions estimated by the neural network based on this knowledge.

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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 1, Issue 3
September 2017
2023 pages
EISSN:2474-9567
DOI:10.1145/3139486
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 11 September 2017
Accepted: 01 July 2017
Revised: 01 May 2017
Received: 01 February 2017
Published in IMWUT Volume 1, Issue 3

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Author Tags

  1. Indoor context recognition
  2. Wi-Fi channel state information
  3. deep neural network
  4. open/close event
  5. pattern recognition

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  • Research-article
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  • Refereed

Funding Sources

  • JST CREST JPMJCR15E2, JSPS KAKENHI

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  • (2024)Understanding the Diffraction Model in Static Multipath-Rich Environments for WiFi Sensing System DesignIEEE Transactions on Mobile Computing10.1109/TMC.2024.337770823:11(10393-10410)Online publication date: 1-Nov-2024
  • (2023)SIDAProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109197:3(1-24)Online publication date: 27-Sep-2023
  • (2023)AttFLProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109177:3(1-31)Online publication date: 27-Sep-2023
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