Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning
Abstract Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescents can cause discomfort due to direct contact and are prone to measurer variation. This study aimed to develop a non-invasive, precise, and co...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88098-x |
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| author | Jun Byung Park Jinjoo Choi Jae Yoon Na Seung Hyun Kim Hyun-Kyung Park Seung Yang Sung Ho Cho |
| author_facet | Jun Byung Park Jinjoo Choi Jae Yoon Na Seung Hyun Kim Hyun-Kyung Park Seung Yang Sung Ho Cho |
| author_sort | Jun Byung Park |
| collection | DOAJ |
| description | Abstract Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescents can cause discomfort due to direct contact and are prone to measurer variation. This study aimed to develop a non-invasive, precise, and convenient alternative for WHR measurement and central obesity assessment using frequency modulated continuous wave (FMCW) radar, and to evaluate its accuracy by comparing it with traditional measurement methods. We included 100 participants aged 7–18 and radar data were analyzed using point cloud generation processed through convolutional neural networks for estimating WHR. The radar-based WHR measurements were compared to conventional clinician measurements. Participants were classified into low (WHR < 0.86), moderate (≥ 0.86, < 0.91) and high WHR (≥ 0.91) groups, and the classifications were compared. Strong agreement was observed between the two methods, with an intraclass correlation coefficient of 0.83 (p = 0.023995). The radar system achieved 82% accuracy in classifying participants into the correct abdominal obesity risk groups. Our findings demonstrate that FMCW radar can be a reliable tool for routine monitoring of central obesity. This technology addresses concerns about privacy and discomfort, making it suitable for widespread application in both clinical and non-clinical settings. |
| format | Article |
| id | doaj-art-1d36e05c552f4f3c9577392926e0533f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1d36e05c552f4f3c9577392926e0533f2025-02-02T12:24:40ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-025-88098-xEvaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learningJun Byung Park0Jinjoo Choi1Jae Yoon Na2Seung Hyun Kim3Hyun-Kyung Park4Seung Yang5Sung Ho Cho6Department of Electronic Engineering, Hanyang UniversityDepartment of Pediatrics, Hanyang University College of MedicineDepartment of Pediatrics, Hanyang University College of MedicineDepartment of Pediatrics, Hanyang University College of MedicineDepartment of Pediatrics, Hanyang University College of MedicineDepartment of Pediatrics, Hanyang University College of MedicineDepartment of Electronic Engineering, Hanyang UniversityAbstract Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescents can cause discomfort due to direct contact and are prone to measurer variation. This study aimed to develop a non-invasive, precise, and convenient alternative for WHR measurement and central obesity assessment using frequency modulated continuous wave (FMCW) radar, and to evaluate its accuracy by comparing it with traditional measurement methods. We included 100 participants aged 7–18 and radar data were analyzed using point cloud generation processed through convolutional neural networks for estimating WHR. The radar-based WHR measurements were compared to conventional clinician measurements. Participants were classified into low (WHR < 0.86), moderate (≥ 0.86, < 0.91) and high WHR (≥ 0.91) groups, and the classifications were compared. Strong agreement was observed between the two methods, with an intraclass correlation coefficient of 0.83 (p = 0.023995). The radar system achieved 82% accuracy in classifying participants into the correct abdominal obesity risk groups. Our findings demonstrate that FMCW radar can be a reliable tool for routine monitoring of central obesity. This technology addresses concerns about privacy and discomfort, making it suitable for widespread application in both clinical and non-clinical settings.https://doi.org/10.1038/s41598-025-88098-xWaist-hip ratioBody mass indexObesity, abdominalRadarMetabolic syndromePediatric obesity |
| spellingShingle | Jun Byung Park Jinjoo Choi Jae Yoon Na Seung Hyun Kim Hyun-Kyung Park Seung Yang Sung Ho Cho Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning Scientific Reports Waist-hip ratio Body mass index Obesity, abdominal Radar Metabolic syndrome Pediatric obesity |
| title | Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning |
| title_full | Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning |
| title_fullStr | Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning |
| title_full_unstemmed | Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning |
| title_short | Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning |
| title_sort | evaluating waist to hip ratio in youth using frequency modulated continuous wave radar and machine learning |
| topic | Waist-hip ratio Body mass index Obesity, abdominal Radar Metabolic syndrome Pediatric obesity |
| url | https://doi.org/10.1038/s41598-025-88098-x |
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