Wireless Sensing for Human Activity Recognition Using USRP

William Taylor; Syed Aziz Shah; Kia Dashtipour; Julien Le Kernec; Qammer H. Abbasi; Khaled Assaleh; Kamran Arshad; Muhammad Ali Imran

doi:10.1007/978-3-030-95593-9_5

Wireless Sensing for Human Activity Recognition Using USRP

William Taylor
, Syed Aziz Shah
, Kia Dashtipour
, Julien Le Kernec
, Qammer H. Abbasi
, Khaled Assaleh
, Kamran Arshad
, Muhammad Ali Imran

University of Glasgow
Coventry University
Ajman University

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

2 Scopus citations

Abstract

Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.

Original language	English
Title of host publication	Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
Editors	Masood Ur Rehman, Ahmed Zoha
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	52-62
Number of pages	11
ISBN (Print)	9783030955922
DOIs	https://doi.org/10.1007/978-3-030-95593-9_5
State	Published - 2022
Event	16th EAI International Conference on Body Area Networks, BODYNETS 2021 - Virtual, Online Duration: 25 Dec 2021 → 26 Dec 2021

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	420 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	16th EAI International Conference on Body Area Networks, BODYNETS 2021
City	Virtual, Online
Period	25/12/21 → 26/12/21

Keywords

Healthcare
RF sensing
Wireless sensing

Access to Document

10.1007/978-3-030-95593-9_5

Cite this

Taylor, W., Shah, S. A., Dashtipour, K., Le Kernec, J., Abbasi, Q. H., Assaleh, K., Arshad, K., & Imran, M. A. (2022). Wireless Sensing for Human Activity Recognition Using USRP. In M. Ur Rehman, & A. Zoha (Eds.), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings (pp. 52-62). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-95593-9_5

Taylor, William ; Shah, Syed Aziz ; Dashtipour, Kia et al. / Wireless Sensing for Human Activity Recognition Using USRP. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. editor / Masood Ur Rehman ; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. pp. 52-62 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{a096884d5a9f4f408b4b529fe4ec1f4c,

title = "Wireless Sensing for Human Activity Recognition Using USRP",

abstract = "Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70\% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.",

keywords = "Healthcare, RF sensing, Wireless sensing",

author = "William Taylor and Shah, \{Syed Aziz\} and Kia Dashtipour and \{Le Kernec\}, Julien and Abbasi, \{Qammer H.\} and Khaled Assaleh and Kamran Arshad and Imran, \{Muhammad Ali\}",

note = "Publisher Copyright: {\textcopyright} 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 16th EAI International Conference on Body Area Networks, BODYNETS 2021 ; Conference date: 25-12-2021 Through 26-12-2021",

year = "2022",

doi = "10.1007/978-3-030-95593-9\_5",

language = "English",

isbn = "9783030955922",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "52--62",

editor = "\{Ur Rehman\}, Masood and Ahmed Zoha",

booktitle = "Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings",

address = "Germany",

}

Taylor, W, Shah, SA, Dashtipour, K, Le Kernec, J, Abbasi, QH, Assaleh, K , Arshad, K & Imran, MA 2022, Wireless Sensing for Human Activity Recognition Using USRP. in M Ur Rehman & A Zoha (eds), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 420 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 52-62, 16th EAI International Conference on Body Area Networks, BODYNETS 2021, Virtual, Online, 25/12/21. https://doi.org/10.1007/978-3-030-95593-9_5

Wireless Sensing for Human Activity Recognition Using USRP. / Taylor, William; Shah, Syed Aziz; Dashtipour, Kia et al.
Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. ed. / Masood Ur Rehman; Ahmed Zoha. Springer Science and Business Media Deutschland GmbH, 2022. p. 52-62 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 420 LNICST).

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

TY - GEN

T1 - Wireless Sensing for Human Activity Recognition Using USRP

AU - Taylor, William

AU - Shah, Syed Aziz

AU - Dashtipour, Kia

AU - Le Kernec, Julien

AU - Abbasi, Qammer H.

AU - Assaleh, Khaled

AU - Arshad, Kamran

AU - Imran, Muhammad Ali

PY - 2022

Y1 - 2022

N2 - Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.

AB - Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.

KW - Healthcare

KW - RF sensing

KW - Wireless sensing

UR - https://www.scopus.com/pages/publications/85125257592

U2 - 10.1007/978-3-030-95593-9_5

DO - 10.1007/978-3-030-95593-9_5

M3 - Conference contribution

AN - SCOPUS:85125257592

SN - 9783030955922

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 52

EP - 62

BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings

A2 - Ur Rehman, Masood

A2 - Zoha, Ahmed

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th EAI International Conference on Body Area Networks, BODYNETS 2021

Y2 - 25 December 2021 through 26 December 2021

ER -

Taylor W, Shah SA, Dashtipour K, Le Kernec J, Abbasi QH, Assaleh K et al. Wireless Sensing for Human Activity Recognition Using USRP. In Ur Rehman M, Zoha A, editors, Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 52-62. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-95593-9_5

Wireless Sensing for Human Activity Recognition Using USRP

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