[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-457082-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-457082-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","artificial-intelligence-based-finetuning-model-for-fall-activity-recognition-in-disabled-persons-within-an-iot-environment-paper","Artificial Intelligence-based finetuning model for fall activity recognition in disabled persons within an IoT environment - paper","","Remote monitoring of fall actions and daily life conditions for disabled individuals is a key driver in telemedicine. The Internet of Things (IoT) combined with artificial intelligence, using deep learning and machine learning, is increasingly applied to automate abnormal and unhealthy-condition detection. Fall detection and human action recognition remain difficult due to complex movements. This paper introduces a Temporal Convolutional Network-Based Fall Activity Recognition System for Disabled Persons (TCN-FARSDP) for IoT use, including Gaussian filtering preprocessing, fused feature extraction (NASNetMobile, DenseNet121, MobileNetV3Large), temporal convolutional classification, and Adamax fine-tuning.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/artificial-intelligence-based-finetuning-model-for-fall-activity-recognition-in-disabled-persons-within-an-iot-environment-paper/457082/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/artificial-intelligence-based-finetuning-model-for-fall-activity-recognition-in-disabled-persons-within-an-iot-environment-paper/457082.png","ImageObject",300,407,{"name":42,"@type":43},"Kyle","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the paper address in an IoT telemedicine context?","Question",{"text":63,"@type":64},"It targets monitoring and detecting fall incidents among disabled persons using an IoT environment for remote, timely investigation of fall-related actions or conditions.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does TCN-FARSDP process input and extract features?",{"text":68,"@type":64},"It first performs image pre-processing using Gaussian filtering to remove noise and improve clarity, then fuses features extracted by NASNetMobile, DenseNet121, and MobileNetV3Large.",{"name":70,"@type":61,"acceptedAnswer":71},"How is the final detection model trained and evaluated?",{"text":72,"@type":64},"A temporal convolutional network (TCN) classifier performs fall activity detection, and fine-tuning is done using Adamax to improve convergence and stability; evaluation uses an FD dataset with reported accuracy of 99.48%.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},457082,1791430294,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,100,105,109,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},7,40,"healthcare",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},8,"Research & Report",30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":55,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":127,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":144,"preview_page_count":4},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nArtificial Intelligence-based finetuning model for fall activity recognition in disabled persons within an IoT environment  \nAbdulrahman Alzahrani1,2􀀍, Reham Al-Dayil3, Amirah GhanimAlghanim4 & Mahir Mohammed Sharif5  \nRemote monitoring offall actions or conditions and the everyday lifecycle of disabled losses is the vital drive of current telemedicine. The Internet of Things (IoT) and Artificial Intelligence (AI) models, which incorporate deep learning (DL) and machine learning (ML) techniques, are increasingly applied in healthcare to automate the detection of abnormal and unhealthy conditions. Fall detection (FD) in elderly patients and human action recognition for surveillance are crucial for safety, but achieving high accuracy remains challenging due to complex human movements. Detecting falls is crucial for healthcare and well-being. This paper presents a novel Temporal Convolutional Network-Based Fall Activity Recognition System for Disabled Persons (TCN-FARSDP) technique designed for use in an IoT Environment. The aim is to monitor and detect fall incidents among disabled persons. Initially, theTCN-FARSDP method performs the image pre-processing stage using Gaussian filtering (GF) to eliminate noise and improve the image clarity. Next, the fusion of feature extraction models involves three techniques: NASNetMobile, DenseNet121, and MobileNetV3Large. For the detection offall activities, the temporal convolutional network (TCN) classifier is employed. Finally, fine-tuning is performed using the Adamax to enhance the convergence and stability of the model. The performance evaluation oftheTCN-FARSDP approach is implemented under an FD dataset. The experimental validation oftheTCN-FARSDP approach portrayed a superior accuracy value of 99.48% over existing techniques.  \nKeywords Internet of things, Disabled persons, Fall activity recognition, Temporal convolutional network, Fusion models  \nFalling is a primary concern for disabled and elderly persons who live alone. They require dependable investigation to support the fall occurrence1. An efficient fall recognition method was needed to present good attention to such individuals, as it will permit interaction with caretakers2. Such strategies will minimize the clinical cost linked to falls and also the lowest death rate amongst disabled and elderly persons because of falls. This higher percentage offall events among the elderly makes road traffic damage the most common cause of death3. All over the world, fall events are considered a public health concern for older adults. The independent life of a disabled person is changed radically after a fall. Generally, these emergencies happen without warning, and it is essential to identify the event of emergencies earlier2. Thus, fall recognition methods are improved, and these methods area lifesaver in clinical warning systems. So, a constant fall recognition method is needed to avoid the significant consequences of a fall4. One of the most common research areas is the human activity recognition (HAR) field5. The accessibility of sensors and accelerometers, combined with minimum cost, lower energy consumption, and advancements in computer vision (CV), AI, and IoT applications, enabled the development of a human-centred monitoring method. This method was used to detect, identify, and categorize human behaviour. HAR became a vital tool for monitoring an individual’s actions, and it is completed by employing DL and ML methods6.  \n1Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin, Saudi Arabia. 2King Salman Centre for Disability Research, Riyadh 11614, Saudi Arabia.  \n3Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. 4College of Computer and Information Sciences, Prince Sultan University, P.O. Bo","cbCaigtgkuVBNbHP","https://ap.wps.com/l/cbCaigtgkuVBNbHP","pdf",4767776,"English","# Introduction\n## Telemedicine, IoT, and AI for monitoring\n## Fall detection challenges\n# Proposed TCN-FARSDP approach\n## Pre-processing with Gaussian filtering\n## Fusion feature extraction models\n## Temporal convolutional network classification and fine-tuning\n# Experimental evaluation\n## Dataset and reported accuracy","[{\"question\":\"What problem does the paper address in an IoT telemedicine context?\",\"answer\":\"It targets monitoring and detecting fall incidents among disabled persons using an IoT environment for remote, timely investigation of fall-related actions or conditions.\"},{\"question\":\"How does TCN-FARSDP process input and extract features?\",\"answer\":\"It first performs image pre-processing using Gaussian filtering to remove noise and improve clarity, then fuses features extracted by NASNetMobile, DenseNet121, and MobileNetV3Large.\"},{\"question\":\"How is the final detection model trained and evaluated?\",\"answer\":\"A temporal convolutional network (TCN) classifier performs fall activity detection, and fine-tuning is done using Adamax to improve convergence and stability; evaluation uses an FD dataset with reported accuracy of 99.48%.\"}]","Artificial Intelligence-based finetuning model for fall activity recognition in disabled persons within an IoT environment - paper | PDF",1790748285,48]