[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119891-en":3,"doc-seo-119891-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119891,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques - Abstract","Respiratory diseases require early diagnosis and continuous monitoring, yet common approaches rely on risky physical contact. This study proposes a contactless monitoring system using FMCW radar to capture breathing movements in real time and machine learning to classify respiratory waveforms. Model performance is assessed via Shuffle Split, K-fold, and stratified K-fold cross-validation. Results indicate Random Forest achieves the highest accuracy at 94.6%, while Naïve Bayes provides the shortest processing time of 0.055 seconds. The system shows feasibility and potential for emergency detection and tracking.","Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques  \nAriana Tulus Purnomo1􀀍 , Raffy Frandito2 , Edrick Hansel Limantoro3 , Rafie Djajasoepena4 , Muhammad Agni Catur Bhakti5 , Ding-Bing Lin6  \n1, 3, 5 Computer Science and Informatics, Faculty of Engineering and Technology, Sampoerna University, Indonesia  \n2 Mechanical Engineering, Faculty of Engineering and Technology, Sampoerna University, Indonesia  \n4 Information Systems, Faculty of Engineering and Technology, Sampoerna University, Indonesia  \n6 Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan  \n\n| Informasi Artikel\u003Cbr>Riwayat Artikel\u003Cbr>Diserahkan : 14-12-2023\u003Cbr>Direvisi : 25-12-2023\u003Cbr>Diterima : 03-02-2024\u003Cbr>Kata Kunci:\u003Cbr>Deteksi Pernapasan;\u003Cbr>Klasifikasi; Pembelajaran  Mesin; Radar FMCW \u003Cbr>Keywords :\u003Cbr>Classification, FMCW Radar, Machine Learning, Respiratory Detection. | ABSTRAK |\n| --- | --- |\n|  | Penyakit pernapasan membutuhkan diagnosis dini dan pemantauansecara terus-menerus, dimana metode yang ada melibatkan kontak fisik yang berisiko. Studi ini mengusulkan sistem baru yang menggunakan radar FMCW dan pembelajaran mesin untuk memantau pernapasan tanpa kontak dengan pasien. Radar FMCW dapat mendeteksi gerakan pernapasan secara real-time, sementarapembelajaran mesin dapat mengklasifikasikan gelombang pernapasan. Studi ini mengevaluasi sistem dengan validasi silang Shuffle Split, Kfold, dan Stratified K-fold. Hasilnya menunjukkan bahwa Random Forest memiliki akurasi tertinggi 94,6% dan Naïve Bayes memilikiwaktu terpendek 0,055 detik. Shuffle Split berkinerja terbaik secarakeseluruhan. Studi ini menunjukkan bahwa sistem memilikikelayakan dan potensi untuk deteksi, dan pelacakan penyakit pernapasan dalam kegawatdaruratan. |\n|  | ABSTRACT |\n|  | Respiratory diseases require early diagnosis and continuous monitoring, but existing methods involve risky physical contact. This study proposes a new system that uses FMCW radar and machine learning to monitor breathing without contact. FMCW radar can detect respiratory movements in real-time, while machine learning can classify respiratory waveforms. This study evaluates the system with cross-validation Shuffle Split, Kold, and Stratified Kold. The results show that Random Forest has the highest accuracy of 94.6% andNaïve Bayes has the shortest time of 0.055 seconds. Shuffle Split performs best overall. This study shows the feasibility and potential of the system for the detection, response, and tracking of respiratory diseases in emergencies. |\n| Corresponding Author :\u003Cbr>Ariana Tulus Purnomo\u003Cbr>Computer Science and Informatics, Faculty of Engineering and Technology, Sampoerna University, Indonesia.\u003Cbr>Jl. Raya Pasar Minggu No.Kav. 16, RT.6/RW.9, Pancoran, Kec. Pancoran, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12780\u003Cbr>Email: [ariana.purnomo@sampoernauniversity.ac.id](ariana.purnomo@sampoernauniversity.ac.id) |  |\n\nINTRODUCTION  \nRespiratory diseases pose a substantial global health burden, demanding innovative solutions for early detection and monitoring (Halpin et al. , 2021) . These diseases encompass a range of conditions, including acute respiratory infection, chronic obstructive pulmonary disease (COPD), asthma, and pulmonary fibrosis, which collectively affect millions worldwide. As technology advances, the urgent need for real-time respiratory signal monitoring becomes increasingly evident, enabling the swift detection of symptoms and enhancing the management of patients with these debilitating diseases.  \nInfectious diseases have heightened the demand for non-contact medical devices that enable healthcare practitioners to monitor patients remotely (Lee et al. , 2021) . This shift towards non-invasive and contactless solutions has proven crucial for patients infected with the virus and healthcare workers at an increased risk of exposure of up to 67%(Romero Starke et al. , 2021) . In this context, ra","cbCaia4DTl3LFIxF","https://ap.wps.com/l/cbCaia4DTl3LFIxF","pdf",503452,1,9,"English","en",105,"# Introduction\n## Motivation for non-contact respiratory monitoring\n## Potential of FMCW radar for vital sign detection\n## Importance of breathing-pattern classification\n## Role of machine learning in signal classification","[{\"question\":\"Why are non-contact respiratory monitoring methods needed?\",\"answer\":\"Respiratory diseases require early diagnosis and continuous monitoring, but existing contact-based methods involve physical contact that carries risk for patients and healthcare workers.\"},{\"question\":\"What does the proposed system use to detect and classify breathing?\",\"answer\":\"The system uses FMCW radar to detect respiratory movements in real time and machine learning to classify respiratory waveforms.\"},{\"question\":\"Which machine learning model performed best, and what were the key results?\",\"answer\":\"Random Forest achieved the highest accuracy of 94.6%, while Naïve Bayes had the shortest processing time of 0.055 seconds. Shuffle Split performed best overall in the evaluation.\"}]","Enhancing Respiratory Disease Diagnosis through FMCW Radar and Machine Learning Techniques - Abstract | PDF",1785726862,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-respiratory-disease-diagnosis-through-fmcw-radar-and-machine-learning-techniques-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-respiratory-disease-diagnosis-through-fmcw-radar-and-machine-learning-techniques-abstract/119891/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are non-contact respiratory monitoring methods needed?","Question",{"text":75,"@type":76},"Respiratory diseases require early diagnosis and continuous monitoring, but existing contact-based methods involve physical contact that carries risk for patients and healthcare workers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed system use to detect and classify breathing?",{"text":80,"@type":76},"The system uses FMCW radar to detect respiratory movements in real time and machine learning to classify respiratory waveforms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best, and what were the key results?",{"text":84,"@type":76},"Random Forest achieved the highest accuracy of 94.6%, while Naïve Bayes had the shortest processing time of 0.055 seconds. 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