Respiration detection based on deep learning and WiFi data

Respiration, a vital basis for life, is a key indicator of health status for human being. Recently, with contact-based devices, some breathing signal detection methods have been proposed, which can achieve high precision, high signal to noise ratio performance. However, these methods require users t...

Full description

Saved in:
Bibliographic Details
Main Author: Hu, Jiaxing
Other Authors: Xie Lihua
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/155410
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-155410
record_format dspace
spelling sg-ntu-dr.10356-1554102023-07-04T17:42:51Z Respiration detection based on deep learning and WiFi data Hu, Jiaxing Xie Lihua School of Electrical and Electronic Engineering ELHXIE@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Respiration, a vital basis for life, is a key indicator of health status for human being. Recently, with contact-based devices, some breathing signal detection methods have been proposed, which can achieve high precision, high signal to noise ratio performance. However, these methods require users to be contacted with the devices, which can cause a series of problems, such as hindering the movement of users or bringing some unpleasant feelings to users. Therefore, there is an urgent need to call for a contactless solution to extract respiratory signals. With the popularity of the indoor wireless devices, breath detection with wireless sensors has drawn a lot of attention. However, the multipath effects, which commonly exist in indoor environments, have serious impacts on the propagation of wireless signals, leading to signal attenuation and poor received quality. Moreover, although the channel state information (CSI) can be readily collected from commercial off-the-shelf (COTS) WiFi devices, the phase of the CSI is distorted due to various offsets introduced during the receiving and transmitting of the wireless signals. In this dissertation, we try to resolve the challenges mentioned above, and design a CNN model for indoor respiration detection, which utilizes the CSI data from COTS WiFi devices. The final result shows an accuracy of 96.05% for human respiration detection. Master of Science (Computer Control and Automation) 2022-02-23T01:42:38Z 2022-02-23T01:42:38Z 2021 Thesis-Master by Coursework Hu, J. (2021). Respiration detection based on deep learning and WiFi data. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155410 https://hdl.handle.net/10356/155410 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Hu, Jiaxing
Respiration detection based on deep learning and WiFi data
description Respiration, a vital basis for life, is a key indicator of health status for human being. Recently, with contact-based devices, some breathing signal detection methods have been proposed, which can achieve high precision, high signal to noise ratio performance. However, these methods require users to be contacted with the devices, which can cause a series of problems, such as hindering the movement of users or bringing some unpleasant feelings to users. Therefore, there is an urgent need to call for a contactless solution to extract respiratory signals. With the popularity of the indoor wireless devices, breath detection with wireless sensors has drawn a lot of attention. However, the multipath effects, which commonly exist in indoor environments, have serious impacts on the propagation of wireless signals, leading to signal attenuation and poor received quality. Moreover, although the channel state information (CSI) can be readily collected from commercial off-the-shelf (COTS) WiFi devices, the phase of the CSI is distorted due to various offsets introduced during the receiving and transmitting of the wireless signals. In this dissertation, we try to resolve the challenges mentioned above, and design a CNN model for indoor respiration detection, which utilizes the CSI data from COTS WiFi devices. The final result shows an accuracy of 96.05% for human respiration detection.
author2 Xie Lihua
author_facet Xie Lihua
Hu, Jiaxing
format Thesis-Master by Coursework
author Hu, Jiaxing
author_sort Hu, Jiaxing
title Respiration detection based on deep learning and WiFi data
title_short Respiration detection based on deep learning and WiFi data
title_full Respiration detection based on deep learning and WiFi data
title_fullStr Respiration detection based on deep learning and WiFi data
title_full_unstemmed Respiration detection based on deep learning and WiFi data
title_sort respiration detection based on deep learning and wifi data
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/155410
_version_ 1772825319846182912