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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021
EditorsMasood Ur Rehman, Ahmed Zoha
PublisherSpringer
Pages3-14
Number of pages12
ISBN (Electronic)9783030955939
ISBN (Print)9783030955922
DOIs
Publication statusPublished - 11 Feb 2022
Event16th EAI International Conference on Body Area Networks 2021 - Virtual, Online
Duration: 25 Dec 202126 Dec 2021

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Volume420
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference16th EAI International Conference on Body Area Networks 2021
Abbreviated titleBODYNETS 2021
CityVirtual, Online
Period25/12/2126/12/21

Keywords

  • Artificial intelligence
  • Data fusion
  • Human activity recognition
  • Signal processing

ASJC Scopus subject areas

  • Computer Networks and Communications

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