Publication:
Non-Contact Vital Sign Monitoring During Sleep Through UWB Radar

cris.author.scopus-author-id 57200084537
cris.author.scopus-author-id 57192974774
cris.author.scopus-author-id 36560557700
cris.author.scopus-author-id 57110320100
cris.author.scopus-author-id 57776252700
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 0a657b50-c6c1-403e-ac43-c1c3eed50be2
cris.virtualsource.department db7b85b7-3dd9-408d-9d57-03e40dcbf1c9
dc.contributor.author Muhammad Husaini
dc.contributor.author Latifah Munirah Kamarudin
dc.contributor.author Ammar Zakaria
dc.contributor.author Kamarudin I.K.
dc.contributor.author Ibrahim M.A.
dc.date.accessioned 2024-09-26T08:50:35Z
dc.date.available 2024-09-26T08:50:35Z
dc.date.issued 2022-01-01
dc.description.abstract To date, ultra-wideband (UWB) radar is one of the leading technologies applied in the field of non-contact vital sign monitoring. A number of studies have focused on processing reflected UWB pulse signals into breathing and heart activities; however, most have emphasised the use of stationary subjects in their data collection processes. Therefore, this paper presents a feasible study conducted to extract the human vital signs of a non-stationary subject during sleep and the optimum position of the UWB radar. The proposed algorithm could measure the respiration rate (RR) and heart rate (HR) recorded regardless of any random body movements during sleep. An analysis of the entire slow time region for the signal was performed to remove random movement signals from the subject by implementing a sinusoidal fitting algorithm to monitor the periodic movement of the chest wall. Next, the value of R-squared was used to find the fit between the algorithm and output signals, and then, the signal was transformed into a frequency domain via Fourier transform (FT). This allowed the determination of the dominant peak from the breathing and heart rates before changing to the rate per minute. An experiment was also done to monitor three different UWB radar positions (i.e. top, side, and bottom of the bed) to identify its optimum location during sleep. All results were then compared with the polysomnography signal. The result found that the top position has the lowest error rate percentage for both RR and HR with only 0.72% and 3.71%, respectively.
dc.identifier.doi 10.1109/ICoICT55009.2022.9914876
dc.identifier.isbn [9781665481656]
dc.identifier.scopus 2-s2.0-85141553839
dc.identifier.uri https://hdl.handle.net/20.500.14170/3827
dc.relation.grantno undefined
dc.relation.ispartof 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
dc.relation.ispartofseries 2022 10th International Conference on Information and Communication Technology, ICoICT 2022
dc.subject Breathing rate | Heart rate | Non-contact | Polysomnography (PSG) | Ultra-wideband (UWB) radar
dc.title Non-Contact Vital Sign Monitoring During Sleep Through UWB Radar
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.endPage 162
oaire.citation.startPage 157
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Teknologi MARA
oairecerif.affiliation.orgunit Universiti Teknologi MARA
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
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person.identifier.scopus-author-id 57200084537
person.identifier.scopus-author-id 57192974774
person.identifier.scopus-author-id 36560557700
person.identifier.scopus-author-id 57110320100
person.identifier.scopus-author-id 57776252700
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