[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122451-en":3,"doc-seo-122451-105":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},122451,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Fog-Driven IoT and Trust-aware Machine Learning Framework for Early Disability Detection and Rehabilitation in Healthcare Systems","The Internet of Things (IoT) and machine learning support healthcare solutions for early disability detection and rehabilitation by reducing communication gaps between patients and providers through efficient medical resource management and timely event handling. Trust management across interconnected medical devices remains a key research challenge, often causing inconsistent data that delays intervention and lowers care quality. A fog-driven trusted disability detection healthcare (FTDD-HC) framework is introduced with wearable sensors to monitor health and movement, classifying normal versus abnormal motion patterns. Implemented in Python, the approach improves detection performance over existing healthcare applications and enables clinicians to deliver more accurate, reliable, and personalized rehabilitation strategies.","Journal of Disability Research  \n2025 | Volume 4 | Pages: 1–10 | e-location ID: e20250717  \nDOI: 10.57197/JDR-2025-0717  \nFog-Driven IoT and Trust-aware Machine Learning Framework for Early Disability Detection and Rehabilitation in Healthcare Systems  \nMalak Alamri1,2 , Khalid Haseeb3 , Mamoona Humayun4 ,* and Naeem Ramzan5  \n1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia  \n2King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia  \n3Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan  \n4School of Computing, Engineering and the Built Environment, University of Roehampton, London, UK  \n5School of Computing, Engineering and Physical Sciences, University of West of Scotland, Paisley PA1 2BE, Scotland, UK  \nCorrespondence to:  \nMamoona Humayun*, [e-mail: mamoona.humayun@roehampton.ac.uk](e-mail: mamoona.humayun@roehampton.ac.uk)  \nReceived: June 16 2025; Revised: September 15 2025; Accepted: September 29 2025; Published Online: October 15 2025  \nABSTRACT  \nThe Internet of Things (IoT) and machine learning offer promising healthcare solutions for enhancing the functionalities of early detection and rehabilitation of disabilities. These systems minimize communication gaps between patients and healthcare providers by efficiently managing medical resources and providing timely responses to specific events. However, most approaches still face research challenges in managing the trust among interconnected medical devices, resulting in inconsistent data, leading to suboptimal care and delayed interventions for individuals with disabilities. To address these limitations, our research explores machine learning classification to introduce a fog-driven trusted disability detection healthcare (FTDD-HC) framework in an IoT environment, which enables timely responses by leveraging wearable sensors to monitor patient health and movement, thereby supporting clinicians in making informed, timely decisions for the early detection of disabilities. In addition, by incorporating trust, our proposed framework enables healthcare providers to develop more accurate, reliable, and personalized rehabilitation strategies for detecting disabilities accurately using real-time patient data. By leveraging machine learning, the proposed framework explores motion-related health metrics. It enhances the disability detection system by classifying normal and abnormal movement patterns, thus providing critical insights into patient conditions. The proposed framework is implemented in Python, and its performance results reveal a significant enhancement over existing healthcare applications, advancing the quality of care for patients with more effective rehabilitation support and early detection of disabilities.  \nKEYWORDS  \nClassification, Disability, Data accuracy, Healthcare system, Machine learning, IoT technologies  \nINTRODUCTION  \nThe Internet of Medical Things (IoMT) (Razdan and Sharma, 2021; Huang et al., 2023) is integrated with future networks to develop healthcare systems that continuously monitor patient conditions and report them to a cloud platform (Mathkor et al., 2024; El-Saleh et al., 2025; Naif Alwakid et al., 2025) . It enables a timely response with personalized healthcare by sensing and forwarding crucial medical records to experts and processing stations. These systems enhance real-time monitoring and reduce the frequency of hospital checkups by utilizing decision-making strategies, resulting in improved system outcomes and effective treatment (Rath et al., 2024; Villegas-Ch, Garca-Ortiz and Sánchez-Viteri, 2024; Syed Muhammad Ali et al., 2025) . The wearable sensors continuously monitor the patient’s body and collect  \nvital physiological data, such as motion patterns, heart rate, blood pressure, and temperature, for health analysis based on recommendations from medical experts (Jegan and Nimi, 2024; Yang, Amin and Shihada, 2024) . T","cbCaine8elEeQaTh","https://ap.wps.com/l/cbCaine8elEeQaTh","pdf",2834358,1,10,"English","en",105,"# Introduction\n## Internet of Medical Things for continuous monitoring\n## Fog-layer processing and real-time condition identification\n## Trust and security challenges in disability pattern detection","[{\"question\":\"What problem does the fog-driven framework target in disability detection?\",\"answer\":\"It targets unreliable outcomes caused by insufficient trust management among interconnected medical devices, which can produce inconsistent sensor data and lead to delayed, suboptimal interventions.\"},{\"question\":\"How does the framework support early disability detection and rehabilitation?\",\"answer\":\"Wearable sensors collect motion- and health-related metrics, which are processed using a fog-driven approach and machine learning classification to detect abnormal movement patterns and inform timely clinical decisions for rehabilitation.\"},{\"question\":\"Which machine learning method is used for classifying disability-related patterns?\",\"answer\":\"The framework uses the k-nearest neighbors (K-NN) algorithm to classify disabilities based on patient conditions.\"}]","Fog-Driven IoT and Trust-aware Machine Learning Framework for Early Disability Detection and Rehabilitation in Healthcare Systems | 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problem does the fog-driven framework target in disability detection?","Question",{"text":75,"@type":76},"It targets unreliable outcomes caused by insufficient trust management among interconnected medical devices, which can produce inconsistent sensor data and lead to delayed, suboptimal interventions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework support early disability detection and rehabilitation?",{"text":80,"@type":76},"Wearable sensors collect motion- and health-related metrics, which are processed using a fog-driven approach and machine learning classification to detect abnormal movement patterns and inform timely clinical decisions for rehabilitation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method is used for classifying disability-related patterns?",{"text":84,"@type":76},"The framework uses the k-nearest neighbors (K-NN) algorithm to classify disabilities based on patient 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