Path loss and human body absorption experiment for breath detection

Fathul Muta, Catur Apriono

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

3 Citations (Scopus)

Abstract

On this day, various applications have been developed by taking advantage of micro-doppler radar. In medical field utilization, micro-doppler radar is useful to measure vital human signs. It measures patients by radiating electromagnetic waves without wasting time to set up equipment on a patient body. Before designing a radar system, all components must be valid to get reliable values. In this research, we study generating and recording radiated signals for detecting human breath by using USRP B200mini. We measure the transmit and receive power in several conditions, such as without object, with an aluminum plate, and a human as the targeted object. From those experiments can estimate lasses due to the targeted object The average path loss when the aluminum plate as the targeted object is 39.87 dB, The average path loss when a human as the targeted object is 44.26 dB. These results indicate that the loss signal due to a human body is 4,39 dB, which is affected by absorption. This insight is useful to determine the optimum distance for a micro-doppler radar used for biomedical applications.

Original languageEnglish
Title of host publicationProceedings of the 2020 27th International Conference on Telecommunications, ICT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728165875
DOIs
Publication statusPublished - 5 Oct 2020
Event27th International Conference on Telecommunications, ICT 2020 - Bali, Indonesia
Duration: 5 Oct 20207 Oct 2020

Publication series

NameProceedings of the 2020 27th International Conference on Telecommunications, ICT 2020

Conference

Conference27th International Conference on Telecommunications, ICT 2020
Country/TerritoryIndonesia
CityBali
Period5/10/207/10/20

Keywords

  • Micro-Doppler Radar
  • Path Loss
  • USRP
  • Vital Sign Detection

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