Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor

Zheqi Yu; Adnan Zahid; William Taylor; Hasan Abbas; Hadi Heidari; Muhammad A. Imran; Qammer H. Abbasi

doi:10.1007/978-3-030-95593-9_1

Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor

Zheqi Yu
, Adnan Zahid
, William Taylor
, Hasan Abbas
, Hadi Heidari
, Muhammad A. Imran
, Qammer H. Abbasi

University of Glasgow
Heriot-Watt University

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

1 Scopus citations

Abstract

This paper proposes a new data fusion method, which uses the designed construction matrix to fuse sensor and USRP data to realise Human Activity Recognition. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripherals are used to collect human activities signals separately. In order to avoid the incompatibility problem with different collection devices, such as different sampling frequency caused inconsistency time axis. The Principal Component Analysis processing the fused data to dimension reduction without time that is performed to extract the time unrelated 5 × 5 feature matrix to represent corresponding activities. There are explores data fusion method between multiple devices and ensures accuracy without dropping. The technique can be extended to other types of hardware signal for data fusion.

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	3-14
Number of pages	12
ISBN (Print)	9783030955922
DOIs	https://doi.org/10.1007/978-3-030-95593-9_1
State	Published - 2022
Externally published	Yes
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

Artificial intelligence
Data fusion
Human activity recognition
Signal processing

Access to Document

10.1007/978-3-030-95593-9_1

Cite this

Yu, Z., Zahid, A., Taylor, W., Abbas, H., Heidari, H., Imran, M. A., & Abbasi, Q. H. (2022). Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor. 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. 3-14). (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_1

Yu, Zheqi ; Zahid, Adnan ; Taylor, William et al. / Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor. 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. 3-14 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{e48403d0786643c2aab4ca772efab986,

title = "Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor",

abstract = "This paper proposes a new data fusion method, which uses the designed construction matrix to fuse sensor and USRP data to realise Human Activity Recognition. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripherals are used to collect human activities signals separately. In order to avoid the incompatibility problem with different collection devices, such as different sampling frequency caused inconsistency time axis. The Principal Component Analysis processing the fused data to dimension reduction without time that is performed to extract the time unrelated 5 × 5 feature matrix to represent corresponding activities. There are explores data fusion method between multiple devices and ensures accuracy without dropping. The technique can be extended to other types of hardware signal for data fusion.",

keywords = "Artificial intelligence, Data fusion, Human activity recognition, Signal processing",

author = "Zheqi Yu and Adnan Zahid and William Taylor and Hasan Abbas and Hadi Heidari and Imran, \{Muhammad A.\} and Abbasi, \{Qammer H.\}",

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\_1",

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 = "3--14",

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",

}

Yu, Z, Zahid, A, Taylor, W, Abbas, H, Heidari, H, Imran, MA & Abbasi, QH 2022, Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor. 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. 3-14, 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_1

Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor. / Yu, Zheqi; Zahid, Adnan; Taylor, William 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. 3-14 (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 - Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor

AU - Yu, Zheqi

AU - Zahid, Adnan

AU - Taylor, William

AU - Abbas, Hasan

AU - Heidari, Hadi

AU - Imran, Muhammad A.

AU - Abbasi, Qammer H.

PY - 2022

Y1 - 2022

N2 - This paper proposes a new data fusion method, which uses the designed construction matrix to fuse sensor and USRP data to realise Human Activity Recognition. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripherals are used to collect human activities signals separately. In order to avoid the incompatibility problem with different collection devices, such as different sampling frequency caused inconsistency time axis. The Principal Component Analysis processing the fused data to dimension reduction without time that is performed to extract the time unrelated 5 × 5 feature matrix to represent corresponding activities. There are explores data fusion method between multiple devices and ensures accuracy without dropping. The technique can be extended to other types of hardware signal for data fusion.

AB - This paper proposes a new data fusion method, which uses the designed construction matrix to fuse sensor and USRP data to realise Human Activity Recognition. At this point, Inertial Measurement Unit sensors and Universal Software-defined Radio Peripherals are used to collect human activities signals separately. In order to avoid the incompatibility problem with different collection devices, such as different sampling frequency caused inconsistency time axis. The Principal Component Analysis processing the fused data to dimension reduction without time that is performed to extract the time unrelated 5 × 5 feature matrix to represent corresponding activities. There are explores data fusion method between multiple devices and ensures accuracy without dropping. The technique can be extended to other types of hardware signal for data fusion.

KW - Artificial intelligence

KW - Data fusion

KW - Human activity recognition

KW - Signal processing

UR - https://www.scopus.com/pages/publications/85125283398

U2 - 10.1007/978-3-030-95593-9_1

DO - 10.1007/978-3-030-95593-9_1

M3 - Conference contribution

AN - SCOPUS:85125283398

SN - 9783030955922

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

SP - 3

EP - 14

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 -

Yu Z, Zahid A, Taylor W, Abbas H, Heidari H, Imran MA et al. Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor. 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. 3-14. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-95593-9_1

Data Fusion for Human Activity Recognition Based on RF Sensing and IMU Sensor

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this