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
Ajman University

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

2 Citations (Scopus)

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	SpringerOpen
Pages	52-62
Number of pages	11
ISBN (Electronic)	9783030955939
ISBN (Print)	9783030955922
DOIs	https://doi.org/10.1007/978-3-030-95593-9_5
Publication status	E-pub ahead of print - 11 Feb 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

Bibliographical note

Funding Information:
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. This work is supported in part by the Ajman University Internal Research Grant.

Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Funding

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. This work is supported in part by the Ajman University Internal Research Grant.

Funders	Funder number
CENSIS UK
Scottish Funding Council
Engineering and Physical Sciences Research Council	EP/T021020/1, EP/T021063/1
Ajman University
British Telecommunications PLC

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Healthcare
RF sensing
Wireless sensing

ASJC Scopus subject areas

Computer Networks and Communications

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). SpringerOpen. Advance online publication. 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. SpringerOpen, 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 proceeding › peer-review

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, SpringerOpen, 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

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. SpringerOpen. 2022. p. 52-62. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). Epub 2022 Feb 11. doi: 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. SpringerOpen, 2022. pp. 52-62 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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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.",

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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.

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Wireless Sensing for Human Activity Recognition Using USRP

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this