[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-442919-105":59,"doc-detail-442919-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","continuous-contactless-and-multimodal-pain-assessment-during-surgical-interventions","Continuous, Contactless, and Multimodal Pain Assessment During Surgical Interventions","","Continuous, multimodal pain classification is introduced using camera-based data in clinical settings. Facial Action Units (AUs) from video samples are integrated with sequential vital parameters extracted from the same recordings, enabling measurement of heart rate variability (HRV) from video-derived photoplethysmographic signals. Video-derived HRV is validated against ECG-based measurements, then combined with AU features using an automated contactless algorithm for binary pain detection. Logistic regression with leave-one-out cross-validation is evaluated on the BioVid Heat Pain Database and tested on surgical individual patient data, achieving F1-scores of 53% and 48% with ultra-short-term processing. The approach uses 5.5 s camera segments for low-latency multimodal pain assessment.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/continuous-contactless-and-multimodal-pain-assessment-during-surgical-interventions/442919/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/continuous-contactless-and-multimodal-pain-assessment-during-surgical-interventions/442919.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How is pain classified in the proposed method?","Question",{"text":113,"@type":114},"Pain is detected using a continuous, contactless multimodal algorithm that combines facial Action Units (AUs) with HRV parameters derived from video-based rPPG signals, then performs binary classification with logistic regression.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are camera-derived HRV signals validated?",{"text":118,"@type":114},"The method validates HRV computed from video-derived photoplethysmographic signals against traditional sensor-based electrocardiogram (ECG) measurements.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance is achieved on the datasets and with what processing duration?",{"text":122,"@type":114},"The approach achieves an F1-score of 53% on the BioVid Heat Pain Database and 48% on individual surgical patient data, using ultra-short-term processing based on 5.5 s camera recordings.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},442919,1790904833,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Continuous, Contactless, and Multimodal Pain Assessment During Surgical  \nInterventions  \nBianca Reichard , Mirco Fuchs , and Kerstin Bode  \nAbstract—Goal: We introduce a continuous, multimodal pain classification technique that utilizes camera-based data conducted in clinical settings. Methods: We integrate facial Action Units (AUs) obtained from samples with sequential vital parameters extracted from video data, and systematically validate the practicality of measuring heart rate variability (HRV) from video-derived photoplethysmographic signals against traditional sensor-based electrocardiogram measurements. Video-based AUs and HRV metrics acquired from ultra-short-term processing are combined into an automated, contactless, multimodal algorithm for binary pain classification. Utilizing logistic regression alongside leave-one-out cross-validation, this approach is developed and validated using the BioVid Heat Pain Database and subsequently tested with our surgical Individual Patient Data. Results: We achieve an F1-score of 53% on the BioVid Heat Pain Database and 48% on our Individual Patient Data with ultra-short-term processing. Conclusion: Our approach provides a robust foundation for future multimodal pain classification utilizing vital signs and mimic parameters from 5.5 s camera recordings.  \nIndex Terms—Clinical data, contactless pain classification, FACS, HRV ultra-short-term processing, multimodal.  \nImpact Statement—We developed a contactless, continuous, and multimodal technique for evaluating pain in clinical settings with low latency. We are the first combining AU data with HRV parameters from camera recordings.  \nReceived 27 April 2025; revised 4 September 2025 and 9 November 2025; accepted 9 November 2025 . Date of publication 14 November 2025; date of current version 4 December 2025 . The review of this article was arranged by Editor Yuteng Xiao. (Corresponding author: Bianca Reichard.)  \nThis work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Ethics Committee at the Medical Faculty of the Leipzig University, and performed in line with the Declaration of Helsinki.  \nBianca Reichard is with the Laboratory for Biosignal Processing, Institute for Applied Informatics and the Leipzig University of Applied Sciences, 04277 Leipzig, Germany (e-mail: bianca.reichard@ [htwk-leipzig.de](htwk-leipzig.de)).  \nMirco Fuchs is with the Laboratory for Biosignal Processing, Leipzig University of Applied Sciences, 04277 Leipzig, Germany (e-mail: [mirco.fuchs@htwk-leipzig.de](mirco.fuchs@htwk-leipzig.de)).  \nKerstin Bode is with the Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, 04109 Leipzig, Germany (e-mail: kerstin.bode@medizin.uni-leipzig.de) .  \nDigital Object Identifier 10.1109/OJEMB.2025.3633051  \nI. INTRODUCTION  \n[ i]t[tem2]n,atni[ng]inemThinntstilndseaettfitrdings pmedndmtsfoedanr micdaercharpea.iImpnperin  \nN RECENT years, pain assessment has gained increased  \nasking patients to use a scale, such as the Numeric Rating Scale (NRS) [4], which rates pain from 0 to 10 . Similarly, the Smiley Analogue Scale (SAS) [5] uses a range from a green smiling face to a red crying emoji which helps children express their pain severity but is unsuitable for those unable to communicate effectively, like infants, people suffering from mental disabilities or dementia. In these cases, the Observer Pain Scale (OPS) [6] allows observers to rate patient pain from 0 to 10. Its effectiveness dependsonskilled staffthat is familiar to the patients. Pain is subjective and difficult to generalize. Regular pain assessment is essential for effective analgesic treatment, as relieving pain and improving well-being enhance and accelerate healing [2] . Unaddressed pain may become chronic, impacting individuals and society severely. However, overtreatment should be avoided due to possible medication side effects, interactions, and addictio","cbCaioFn5SkUC7AW","https://ap.wps.com/l/cbCaioFn5SkUC7AW","pdf",1226388,"English","# Abstract\n# Impact Statement\n# Introduction\n# Materials and Methods\n## Related Work","[{\"question\":\"How is pain classified in the proposed method?\",\"answer\":\"Pain is detected using a continuous, contactless multimodal algorithm that combines facial Action Units (AUs) with HRV parameters derived from video-based rPPG signals, then performs binary classification with logistic regression.\"},{\"question\":\"How are camera-derived HRV signals validated?\",\"answer\":\"The method validates HRV computed from video-derived photoplethysmographic signals against traditional sensor-based electrocardiogram (ECG) measurements.\"},{\"question\":\"What performance is achieved on the datasets and with what processing duration?\",\"answer\":\"The approach achieves an F1-score of 53% on the BioVid Heat Pain Database and 48% on individual surgical patient data, using ultra-short-term processing based on 5.5 s camera recordings.\"}]","Continuous, Contactless, and Multimodal Pain Assessment During Surgical Interventions | PDF",1790702104,15]