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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
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 |