[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121242-en":3,"doc-seo-121242-105":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":20,"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},121242,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Multimodal Pain Recognition in Postoperative Patients - Machine Learning Approach","Acute pain management after surgery requires dependable assessment, especially for patients who may struggle to self-report. Traditional approaches based on patient statements or behavioral observation can produce variability, while prior multimodal research often focuses on controlled settings rather than real postoperative conditions. A multimodal framework is developed and evaluated for objective pain assessment using biosignals—ECG, EMG, electrodermal activity, and respiration rate—captured during light activity in a clinical iHurt study.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nMultimodal Pain Recognition in Postoperative Patients: Machine Learning Approach.  \nPermalink  \n[https://escholarship.org/uc/item/13m9x4rp](https://escholarship.org/uc/item/13m9x4rp)  \nAuthors  \nSubramanian, Ajan  \nCao, Rui Naeini, Emadet al.  \nPublication Date  \n2025-01-27  \nDOI  \n10.2196/67969  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nJMIR FORMATIVE RESEARCH Subramanian et al  \nOriginal Paper  \nMultimodal Pain Recognition in Postoperative Patients: Machine Learning Approach  \n\n| Ajan Subramanian1, MS; Rui Cao2, MS; Emad Kasaeyan Naeini1, PhD; Seyed Amir Hossein Aqajari2, PhD; Thomas D Hughes3, RN; Michael-David Calderon4, MS; Kai Zheng5, PhD; Nikil Dutt 1, PhD; Pasi Liljeberg6, PhD; Sanna Salanterä7,8, RN, PhD; Ariana M Nelson9, MD; Amir M Rahmani1,2,3,10, MBA, PhD |\n| --- |\n| 1Department of Computer Science, University of California, Irvine, Irvine, CA, United States\u003Cbr>2Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States 3School of Nursing, University of California, Irvine, Irvine, CA, United States\u003Cbr>4College of Medicine, Kansas City University, Kansas City, MO, United States 5Department of Informatics, University of California, Irvine, Irvine, CA, United States 6Department of Computing, University of Turku, Turku, Finland\u003Cbr>7Department of Nursing Science, University of Turku, Turku, Finland 8Turku University Hospital, University of Turku, Turku, Finland\u003Cbr>9Department of Anesthesiology and Pain Medicine, University of California, Irvine, Irvine, CA, United States 10Institute for Future Health, University of California, Irvine, Irvine, CA, United States\u003Cbr>Corresponding Author:\u003Cbr>Ajan Subramanian, MS Department of Computer Science University of California, Irvine 3211 Donald Bren Hall\u003Cbr>Irvine, CA, 92617 United States Phone: 1 6506604994\u003Cbr>[Email: ](Email: ajans1@uci.edu)[ajans1@uci.edu](Email: ajans1@uci.edu)\u003Cbr>Abstract |\n\nBackground: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.  \nObjective: This study aimed to develop and evaluate a multimodal machine learning–based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals.  \nMethods: The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanc","cbCaia3vxOoR4lDA","https://ap.wps.com/l/cbCaia3vxOoR4lDA","pdf",834782,1,17,"English","en",105,"# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusions\n# Keywords\n# Introduction","[{\"question\":\"Why is postoperative pain assessment challenging in real clinical settings?\",\"answer\":\"Some postoperative patients cannot reliably self-report pain, and existing assessment methods can vary. Real-world monitoring is also complicated by motion artifacts, imbalanced label distributions, and sparse data.\"},{\"question\":\"What data and biosignals are used for the proposed pain recognition framework?\",\"answer\":\"The iHurt study captures multimodal biosignals including electrocardiogram, electromyogram, electrodermal activity, and respiration rate, alongside pain ratings from the Numerical Rating Scale.\"},{\"question\":\"How well do the multimodal models perform compared with single-modality approaches?\",\"answer\":\"The models achieve average balanced accuracy above 80% across pain levels. Respiration rate models generally outperform other single modalities, while the multimodal framework still exceeds most prior work in overall accuracy.\"}]","Multimodal Pain Recognition in Postoperative Patients - Machine Learning Approach | PDF",1785734529,43,{"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},"multimodal-pain-recognition-in-postoperative-patients-machine-learning-approach","",{"@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/multimodal-pain-recognition-in-postoperative-patients-machine-learning-approach/121242/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is postoperative pain assessment challenging in real clinical settings?","Question",{"text":75,"@type":76},"Some postoperative patients cannot reliably self-report pain, and existing assessment methods can vary. Real-world monitoring is also complicated by motion artifacts, imbalanced label distributions, and sparse data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and biosignals are used for the proposed pain recognition framework?",{"text":80,"@type":76},"The iHurt study captures multimodal biosignals including electrocardiogram, electromyogram, electrodermal activity, and respiration rate, alongside pain ratings from the Numerical Rating Scale.",{"name":82,"@type":73,"acceptedAnswer":83},"How well do the multimodal models perform compared with single-modality approaches?",{"text":84,"@type":76},"The models achieve average balanced accuracy above 80% across pain levels. Respiration rate models generally outperform other single modalities, while the multimodal framework still exceeds most prior work in overall accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]