[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124119-en":3,"doc-seo-124119-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124119,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Enhancing Contactless Respiratory Rate Measurement Accuracy - Integration of 24GHz FMCW Radar and XGBoost Machine Learning","Advancements in non-contact vital sign monitoring are crucial for improving measurement accuracy and patient experience. This research integrates a 24GHz frequency-modulated continuous-wave (FMCW) radar with an XGBoost machine learning model to improve respiratory rate (RR) detection. FMCW radar captures respiratory motion signals, while preprocessing removes noise and irrelevant data and feature extraction prepares inputs for XGBoost. Trained and validated on datasets covering controlled and randomized RR across diverse subjects, the model delivers higher accuracy and reliability than other methods, reducing error margins versus benchmarks. The approach supports continuous, non-intrusive monitoring in environments where contact methods are impractical.","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage : www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nEnhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning  \nArisandy a,*, Bayu Erfianto b, Setyorinib  \na Master of Informatics Study Program, Telkom University, Bandung, Indonesia  \nb Department of Information Technology, Telkom University, Bandung, Indonesia  \nCorresponding author: [arisandyarief@student.telkomuniversity.ac.id](arisandyarief@student.telkomuniversity.ac.id)  \nAbstract—Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, preprocessing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.  \nKeywords—Respiratory rate; radar; FMCW; machine learning; XGBoost.  \nManuscript received 22 Mar. 2024; revised 28 Apr. 2024; accepted 5 Jun. 2024. Date of publication 31 Dec. 2024.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nRespiratory rate (RR), the number of breaths a person takes per minute, is an essential vital sign monitored alongside body temperature, heart rate, and blood pressure. As an indicator of respiratory function, RR provides critical insights into aperson's health status [1]. In clinical settings, precise RR monitoring helps manage interventions, assists in recognizing early signs of decline, and supports the diagnosis of respiratory diseases [2]. The COVID-19 pandemic has sharply highlighted the need for accurate and reliable RR monitoring techniques, especially those that limit physical contact to reduce the risk of infection transmission [3] .  \nCurrently, various methods are available to measure respiratory rate (RR) . Contact-based methods, such as piezoelectric sensors, pneumotachographs, and impedance pneumography, provide accurate measurements by directly detecting respiratory effort [4], [5], [6] . However, these  \nmethods require physical contact with the patient, which can cause discomfort and raise concerns about hygiene and infection transmission. On the other hand, contactless methods like ballistocardiograph, radar, and thermal imaging focus on patient comfort and safety by avoiding direct contact [7], [8], [9]. Yet, these methods often need","cbCaivaRJ5q52AUz","https://ap.wps.com/l/cbCaivaRJ5q52AUz","pdf",4290302,1,"English","en",105,"# Introduction\n## Respiratory rate and clinical importance\n## Contact-based vs contactless measurement methods\n## Radar approaches and FMCW advantages","[{\"question\":\"How does the 24GHz FMCW radar contribute to respiratory rate measurement?\",\"answer\":\"The 24GHz FMCW radar detects respiratory motion by capturing signal changes related to breathing. Its chirp-based transmission enables effective signal processing for RR estimation.\"},{\"question\":\"What role does XGBoost play in improving measurement accuracy?\",\"answer\":\"XGBoost analyzes extracted signal features after noise and irrelevant data are removed. This machine learning step improves detection precision and robustness.\"},{\"question\":\"What evidence shows the integrated method outperforms other models?\",\"answer\":\"Empirical results indicate XGBoost achieves higher accuracy and reliability than other machine learning models, with significantly reduced error margins compared with established benchmarks.\"}]","Enhancing Contactless Respiratory Rate Measurement Accuracy - Integration of 24GHz FMCW Radar and XGBoost Machine Learning | PDF",1785820549,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"enhancing-contactless-respiratory-rate-measurement-accuracy-integration-of-24ghz-fmcw-radar-and-xgboost-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/enhancing-contactless-respiratory-rate-measurement-accuracy-integration-of-24ghz-fmcw-radar-and-xgboost-machine-learning/124119/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the 24GHz FMCW radar contribute to respiratory rate measurement?","Question",{"text":74,"@type":75},"The 24GHz FMCW radar detects respiratory motion by capturing signal changes related to breathing. Its chirp-based transmission enables effective signal processing for RR estimation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What role does XGBoost play in improving measurement accuracy?",{"text":79,"@type":75},"XGBoost analyzes extracted signal features after noise and irrelevant data are removed. This machine learning step improves detection precision and robustness.",{"name":81,"@type":72,"acceptedAnswer":82},"What evidence shows the integrated method outperforms other models?",{"text":83,"@type":75},"Empirical results indicate XGBoost achieves higher accuracy and reliability than other machine learning models, with significantly reduced error margins compared with established benchmarks.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]