Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10445474/ |
| _version_ | 1797277619532595200 |
|---|---|
| author | Ryota Goto Taichi Horimoto Shotaro Koyama Tsubasa Suzuki Junpei Tsutsumi Taisei Matsuyama Masaya Hasegawa Shigeki Hirobayashi Kazuo Yoshida |
| author_facet | Ryota Goto Taichi Horimoto Shotaro Koyama Tsubasa Suzuki Junpei Tsutsumi Taisei Matsuyama Masaya Hasegawa Shigeki Hirobayashi Kazuo Yoshida |
| author_sort | Ryota Goto |
| collection | DOAJ |
| description | The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions. |
| first_indexed | 2024-03-07T15:51:59Z |
| format | Article |
| id | doaj.art-161342661c26413ba7a2ebc91cf14a59 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T15:51:59Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-161342661c26413ba7a2ebc91cf14a592024-03-05T00:01:29ZengIEEEIEEE Access2169-35362024-01-0112323493236010.1109/ACCESS.2024.337067110445474Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic AnalysisRyota Goto0https://orcid.org/0009-0002-9719-0623Taichi Horimoto1https://orcid.org/0000-0002-3877-5341Shotaro Koyama2Tsubasa Suzuki3Junpei Tsutsumi4Taisei Matsuyama5Masaya Hasegawa6Shigeki Hirobayashi7https://orcid.org/0000-0001-6402-7382Kazuo Yoshida8Department of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanDepartment of Intellectual Information Systems Engineering, University of Toyama, Toyama, JapanCarea Corporation, 4F Toyamashishinsangyoshiensenta, Toyama, JapanThe spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.https://ieeexplore.ieee.org/document/10445474/Continuous-wave Doppler radar (CW Doppler radar)driving simulationharmonicheartbeatnon-harmonic analysis (NHA) |
| spellingShingle | Ryota Goto Taichi Horimoto Shotaro Koyama Tsubasa Suzuki Junpei Tsutsumi Taisei Matsuyama Masaya Hasegawa Shigeki Hirobayashi Kazuo Yoshida Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis IEEE Access Continuous-wave Doppler radar (CW Doppler radar) driving simulation harmonic heartbeat non-harmonic analysis (NHA) |
| title | Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis |
| title_full | Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis |
| title_fullStr | Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis |
| title_full_unstemmed | Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis |
| title_short | Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis |
| title_sort | detection of heartbeat components through doppler radar systems using semantic segmentation and non harmonic analysis |
| topic | Continuous-wave Doppler radar (CW Doppler radar) driving simulation harmonic heartbeat non-harmonic analysis (NHA) |
| url | https://ieeexplore.ieee.org/document/10445474/ |
| work_keys_str_mv | AT ryotagoto detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT taichihorimoto detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT shotarokoyama detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT tsubasasuzuki detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT junpeitsutsumi detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT taiseimatsuyama detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT masayahasegawa detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT shigekihirobayashi detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis AT kazuoyoshida detectionofheartbeatcomponentsthroughdopplerradarsystemsusingsemanticsegmentationandnonharmonicanalysis |