RF Based Real Time Human Motion Sensing

William Taylor; Ahmad Taha; Kia Dashtipour; Syed Aziz Shah; Qammer H. Abbasi; Muhammad Ali Imran

doi:10.1109/aps/ursi47566.2021.9703954

RF Based Real Time Human Motion Sensing

William Taylor, Ahmad Taha, Kia Dashtipour, Syed Aziz Shah, Qammer H. Abbasi, Muhammad Ali Imran

University of Glasgow

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

Recent research has shown that the propagation of Radio Frequencies signals is affected by human movements taking place between the RF transmitter and receiver antennas. Artificial intelligence has been widely used to classify the patterns of signal propagation. With the help of a universal software radio peripheral device, a system was developed based on a real-time machine learning classification algorithm to ensure alerts of incidents are received in a timely manner. The machine learning model was built to distinguish between “No Activity” and “Movement” status of a single human subject. The model recorded a high classification accuracy of 97.8 % which enabled an accurate classification of new data in real-time.

Original language	English
Title of host publication	2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings
Publisher	IEEE
Pages	2044-2045
Number of pages	2
ISBN (Electronic)	978-1-7281-4670-6
DOIs	https://doi.org/10.1109/aps/ursi47566.2021.9703954
Publication status	Published - 16 Feb 2022
Event	2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting - Singapore, Singapore Duration: 4 Dec 2021 → 10 Dec 2021 https://2021apsursi.org/

Publication series

Name	2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings

Conference

Conference	2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting
Abbreviated title	APS/URSI
Country/Territory	Singapore
City	Singapore
Period	4/12/21 → 10/12/21
Internet address	https://2021apsursi.org/

Bibliographical note

Funding Information:
V. ACKNOWLEDGEMENT William Taylor’s studentship is funded by CENSIS UK through Scottish funding council in collaboration with British Telecom. This work is supported in parts by EPSRC EP/T021020/1 and EP/T021063/1

Publisher Copyright:
© 2021 IEEE.

Keywords

Human motion detection
Channel State Information
RF signals
Machine Learning
Real-time

Access to Document

10.1109/aps/ursi47566.2021.9703954

Cite this

Taylor, W., Taha, A., Dashtipour, K., Shah, S. A., Abbasi, Q. H., & Imran, M. A. (2022). RF Based Real Time Human Motion Sensing. In 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings (pp. 2044-2045). (2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings). IEEE. https://doi.org/10.1109/aps/ursi47566.2021.9703954

RF Based Real Time Human Motion Sensing. / Taylor, William; Taha, Ahmad; Dashtipour, Kia et al.
2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings. IEEE, 2022. p. 2044-2045 (2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Taylor, W, Taha, A, Dashtipour, K, Shah, SA, Abbasi, QH & Imran, MA 2022, RF Based Real Time Human Motion Sensing. in 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings. 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings, IEEE, pp. 2044-2045, 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, Singapore, Singapore, 4/12/21. https://doi.org/10.1109/aps/ursi47566.2021.9703954

Taylor W, Taha A, Dashtipour K, Shah SA, Abbasi QH, Imran MA. RF Based Real Time Human Motion Sensing. In 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings. IEEE. 2022. p. 2044-2045. (2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings). doi: 10.1109/aps/ursi47566.2021.9703954

Taylor, William ; Taha, Ahmad ; Dashtipour, Kia et al. / RF Based Real Time Human Motion Sensing. 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings. IEEE, 2022. pp. 2044-2045 (2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings).

@inproceedings{5504b9a5829a4fe88ddf1aa29b40f8f2,

title = "RF Based Real Time Human Motion Sensing",

abstract = "Recent research has shown that the propagation of Radio Frequencies signals is affected by human movements taking place between the RF transmitter and receiver antennas. Artificial intelligence has been widely used to classify the patterns of signal propagation. With the help of a universal software radio peripheral device, a system was developed based on a real-time machine learning classification algorithm to ensure alerts of incidents are received in a timely manner. The machine learning model was built to distinguish between “No Activity” and “Movement” status of a single human subject. The model recorded a high classification accuracy of 97.8 % which enabled an accurate classification of new data in real-time.",

keywords = "Human motion detection, Channel State Information, RF signals, Machine Learning, Real-time",

author = "William Taylor and Ahmad Taha and Kia Dashtipour and Shah, {Syed Aziz} and Abbasi, {Qammer H.} and Imran, {Muhammad Ali}",

note = "Funding Information: V. ACKNOWLEDGEMENT William Taylor{\textquoteright}s studentship is funded by CENSIS UK through Scottish funding council in collaboration with British Telecom. This work is supported in parts by EPSRC EP/T021020/1 and EP/T021063/1 Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APS/URSI ; Conference date: 04-12-2021 Through 10-12-2021",

year = "2022",

month = feb,

day = "16",

doi = "10.1109/aps/ursi47566.2021.9703954",

language = "English",

series = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",

publisher = "IEEE",

pages = "2044--2045",

booktitle = "2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings",

address = "United States",

url = "https://2021apsursi.org/",

}

TY - GEN

T1 - RF Based Real Time Human Motion Sensing

AU - Taylor, William

AU - Taha, Ahmad

AU - Dashtipour, Kia

AU - Shah, Syed Aziz

AU - Abbasi, Qammer H.

AU - Imran, Muhammad Ali

N1 - Funding Information: V. ACKNOWLEDGEMENT William Taylor’s studentship is funded by CENSIS UK through Scottish funding council in collaboration with British Telecom. This work is supported in parts by EPSRC EP/T021020/1 and EP/T021063/1 Publisher Copyright: © 2021 IEEE.

PY - 2022/2/16

Y1 - 2022/2/16

N2 - Recent research has shown that the propagation of Radio Frequencies signals is affected by human movements taking place between the RF transmitter and receiver antennas. Artificial intelligence has been widely used to classify the patterns of signal propagation. With the help of a universal software radio peripheral device, a system was developed based on a real-time machine learning classification algorithm to ensure alerts of incidents are received in a timely manner. The machine learning model was built to distinguish between “No Activity” and “Movement” status of a single human subject. The model recorded a high classification accuracy of 97.8 % which enabled an accurate classification of new data in real-time.

AB - Recent research has shown that the propagation of Radio Frequencies signals is affected by human movements taking place between the RF transmitter and receiver antennas. Artificial intelligence has been widely used to classify the patterns of signal propagation. With the help of a universal software radio peripheral device, a system was developed based on a real-time machine learning classification algorithm to ensure alerts of incidents are received in a timely manner. The machine learning model was built to distinguish between “No Activity” and “Movement” status of a single human subject. The model recorded a high classification accuracy of 97.8 % which enabled an accurate classification of new data in real-time.

KW - Human motion detection

KW - Channel State Information

KW - RF signals

KW - Machine Learning

KW - Real-time

UR - http://www.scopus.com/inward/record.url?scp=85126898386&partnerID=8YFLogxK

U2 - 10.1109/aps/ursi47566.2021.9703954

DO - 10.1109/aps/ursi47566.2021.9703954

M3 - Conference proceeding

T3 - 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings

SP - 2044

EP - 2045

BT - 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings

PB - IEEE

T2 - 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

Y2 - 4 December 2021 through 10 December 2021

ER -

RF Based Real Time Human Motion Sensing

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this