[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85369-en":3,"doc-seo-85369-105":29,"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":13,"seo_description":14,"update_tm":27,"read_time":28},85369,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments","A cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework is presented for action and activity recognition in healthcare-oriented training settings. The approach combines parameter-efficient, modality-specific adaptation with sequential fusion, integrating modalities in stages while avoiding retraining of previously learned components. Modalities are fused progressively, first grouping closely related ones before adding heterogeneous signals. Evaluation on NurViD and the Nurse Training dataset shows improved performance over single-modality baselines and competitive results versus dataset-specific references.","LoRA-Based Cascaded Multimodal Fusion for Action Recognition  \nin Medical Training Environments  \nDivya Mereddy✁∗ [divya.mereddy@vanderbilt.edu](divya.mereddy@vanderbilt.edu)[ ](divya.mereddy@vanderbilt.edu)Vanderbilt University Nashville, TN, USA  \nJeevan Beedareddy  \nQuince Mountainview, CA, USA  \nAbstract  \nThis paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-e!cient modality-speci\"c adaptation with sequential fusion, enabling modalities tobe integrated in stages without retraining previously learned components. Rather than assuming a \"xed fusion structure, the framework \"rst integrates more closely related modalities and then incorporates additional heterogeneous modalities, supporting scalable adaptation across datasets with di\\#erent modality sets.We evaluate the framework on two healthcare-oriented training environment datasets: NurViD and the Nurse Training dataset. Across these datasets, preliminary results suggest that the proposed cascaded fusion strategy improves over individual modality models and provides competitive performance relative to previously reported dataset-speci\"c baselines. Overall, these \"ndings indicate that cascaded LoRA-based fusion is a promising parameter-e!cient approach for integrating heterogeneous modalities in medical training action and activity recognition tasks. github: [https://github.com/anonymous0-ai/LoRA-Based-Cascaded](https://github.com/anonymous0-ai/LoRA-Based-Cascaded)Multimodal-Fusion-.git.  \nCCS Concepts  \n• Computing methodologies → Neural networks; Computer vision; Learning latent representations; Machine learning algorithms.  \nKeywords  \nMultimodal learning, Cascaded fusion, Low-Rank Adaptation, LoRA, Action recognition, Activity recognition, Medical training environments  \nACM Reference Format:  \nDivya Mereddy and Jeevan Beedareddy. 2018. LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments. In Proceedings of Make sure to enter the correct conference title from your rights con!rmation email (Conference acronym ’XX). ACM, New York, NY, USA, 5 pages. [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro\"t or commercial advantage and that copies bear this notice and the full citation on the \"rst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior speci\"c permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nConference acronym ’XX, Woodstock, NY  \n© 2018 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/2018/06  \n[https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX)  \n1 Introduction  \nThe ability to accurately recognize and classify human actions is critical across numerous domains, including surveillance, healthcare, education, and training environments. With the increasing complexity of real-world environments, action recognition tasks often require the integration of diverse data modalities, such as skeletal, $ow, RGB, and audio information. Multimodal fusion models have emerged as a promising solution to these challenges by leveraging complementary modalities to provide a richer and more nuanced understanding of activities. However, existing approaches face several limitations, including high computational costs, ine!-ciency in adapting to new modalities, and the need for retraining entire models when datasets evolve.  \nTo address these challenges, we propose","cbCais6ucPoZe5Eg","https://ap.wps.com/l/cbCais6ucPoZe5Eg","pdf",1087735,1,5,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"What problem does the cascaded LoRA-based framework address in medical training environments?\",\"answer\":\"It targets accurate action and activity recognition using multiple modalities in healthcare-oriented training settings, where existing methods are costly and require retraining when datasets or modalities change.\"},{\"question\":\"How does the cascaded fusion approach work in the proposed method?\",\"answer\":\"It performs sequential fusion: modalities that are more closely related are integrated first, and additional heterogeneous modalities are incorporated in later stages without retraining earlier components.\"},{\"question\":\"What datasets are used for evaluation and what do the results indicate?\",\"answer\":\"The framework is evaluated on NurViD and the Nurse Training dataset, where the cascaded strategy improves over individual modality models and remains competitive with previously reported baselines.\"}]",1784202842,13,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"lora-based-cascaded-multimodal-fusion-for-action-recognition-in-medical-training-environments","",{"@graph":35,"@context":85},[36,53,68],{"@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/lora-based-cascaded-multimodal-fusion-for-action-recognition-in-medical-training-environments/85369/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",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},"What problem does the cascaded LoRA-based framework address in medical training environments?","Question",{"text":75,"@type":76},"It targets accurate action and activity recognition using multiple modalities in healthcare-oriented training settings, where existing methods are costly and require retraining when datasets or modalities change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the cascaded fusion approach work in the proposed method?",{"text":80,"@type":76},"It performs sequential fusion: modalities that are more closely related are integrated first, and additional heterogeneous modalities are incorporated in later stages without retraining earlier components.",{"name":82,"@type":73,"acceptedAnswer":83},"What datasets are used for evaluation and what do the results indicate?",{"text":84,"@type":76},"The framework is evaluated on NurViD and the Nurse Training dataset, where the cascaded strategy improves over individual modality models and remains competitive with 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