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RFiDARFusion: Enhancing Contactless Activity Monitoring with Radar and RFID Fusion

  • University of Glasgow
  • National University of Technology
  • Aiman University
  • University of Essex

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Indoor human activity recognition (HAR) faces challenges due to the limitations of single-sensor systems in terms of privacy and cost. Our contactless RFiDARFusion system aims to reduce healthcare costs by swiftly identifying unforeseen activities through artificial intelligence and wireless communication. The system accurately discerns complex human activities by employing the LSTM-VAE fusion algorithm for multi-sensor data extraction, proving effective in long-range and non-line-of-sight environments. Two data fusion algorithms were studied across five distinct activities, significantly improving HAR accuracy by a minimum of 4.5% and 6.5% under data and feature-level fusion, respectively. The resulting RFiDARFusion system is contactless, precise, and robust, promising to enhance the quality of life for elderly patients in assisted living.

Original languageEnglish
Title of host publication2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages689-690
Number of pages2
ISBN (Electronic)9798350369908
DOIs
StatePublished - 2024
Event2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 - Florence, Italy
Duration: 14 Jul 202419 Jul 2024

Publication series

NameIEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
ISSN (Print)1522-3965

Conference

Conference2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024
Country/TerritoryItaly
CityFlorence
Period14/07/2419/07/24

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