[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122234-en":3,"doc-seo-122234-105":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},122234,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","AI and machine learning applications in wearable health devices - Review","AI-enabled wearable health technology combines IoT, cloud computing, and machine learning algorithms to monitor vital signs such as heart rate, blood glucose, and oxygen saturation for real-time tracking. This supports predictive insights, personalized therapies, and remote healthcare, while also enabling early detection of anomalies and disease risk. Key challenges remain data privacy, model generalization, regulatory validation, and accessibility, which can be improved via generative AI and federated learning. Future progress is driven by predictive modeling, AR/VR, and blockchain, supported by multidisciplinary collaboration to deliver scalable, equitable care.","Review Article  \nAI and machine learning applications in wearable health devices  \nMuhammad Nadir Shabbir1,*, Duong Thuy Linh2  \n1 School of Economics, Renmin University of China, Beijing 100872, China  \n2 Faculty of Management Sciences, National Economics University, Hanoi 100000, Vietnam  \n* Corresponding author: Muhammad Nadir Shabbir, [20238018@ruc.edu.cn](20238018@ruc.edu.cn), [muhammadnadir948@gmail.com](muhammadnadir948@gmail.com)   \nABSTRACT  \nBy enabling real-time monitoring, early diagnosis, and tailored therapies, wearable health technology combining artificial intelligence and machine learning has transformed healthcare. Wearables combine Internet of Things (IoT), cloud computing, and artificial intelligence (AI) using algorithms to monitor vital signs, including heart rate, blood glucose levels, and oxygen saturation. This enables predictive insights into health conditions, maximizes therapies, and supports remote healthcare options. Even if their potential use is rather high, data privacy, model generalization, regulatory validation, and accessibility remain issues. Generative artificial intelligence and federated learning both help privacy and performance. Predictive modeling, AR/VR, and blockchain technology will drive wearable health devices forward. Artificial intelligence-powered wearables impact world health since they provide competitively priced, scalable solutions for poor populations. By means of overcoming obstacles, multidisciplinary teamwork provides fair, safe, and changing healthcare service.  \nKeywords: wearable health devices; artificial intelligence; machine learning, health monitoring; technology   \n1. Introduction  \nWearable health technology and artificial intelligence (AI) together represent a radical turn in modern healthcare. Originally used as fitness trackers, wearable devices driven by artificial intelligence have developed into indispensable tools for health monitoring, early diagnosis, and tailored therapy. Thanks to their rising power of artificial intelligence in processing and interpreting vast amounts of real-time data, these devices can already spot anomalies, forecast diseases, and support medical decision-making with hitherto unheard-of accuracy[1] .  \nWearable devices tracking significant health parameters, including heart rate variability, glucose levels, and blood oxygen saturation have inspired concepts in recent breakthroughs in sensor technology, data integration, and machine learning models[2] . Management of chronic diseases, mental health monitoring, and even early diagnosis of acute medical events including respiratory or cardiovascular problems are just a few of the more frequent applications for these technologies [3] . Furthermore, by means of remote monitoring and enhanced access to medical information, wearable artificial intelligence has enormous potential to reduce the load on healthcare institutions, especially in poor areas.  \nARTICLE INFO  \nReceived: 2 October 2024 | Accepted: 23 October 2024 | Available online: 3 November 2024  \nCITATION  \nShabbir MN, Linh DT. AI and machine learning applications in wearable health devices. Wearable Technology 2024; 5(1): 3123. doi: 10.54517/wt3123  \nCOPYRIGHT  \nCopyright © 2024 by author(s) . Wearable Technology is published by Asia Pacific Academy of Science Pte. Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), permitting distribution and reproduction in any medium, provided the original work is cited.  \nNotwithstanding its promise, problems including data privacy, connectivity with present healthcare systems, and the need for robust validation studies still exist. If we are to ensure the public adoption and dependability of wearable health equipment driven by artificial intelligence, so reducing the gap between proactive and reactive healthcare solutions, these issues ","cbCaimSWuiuQG0h6","https://ap.wps.com/l/cbCaimSWuiuQG0h6","pdf",522344,1,10,"English","en",105,"# Introduction\n## AI and ML in wearable healthcare\n# Applications and benefits\n## Chronic disease management\n## Mental health monitoring\n# Challenges and future directions\n## Privacy, validation, and generalization\n## Generative AI, federated learning, AR/VR, blockchain","[{\"question\":\"What capabilities do AI and machine learning add to wearable health devices?\",\"answer\":\"They enable real-time monitoring, interpretation of large data streams, anomaly spotting, disease forecasting, and support for medical decision-making with higher accuracy.\"},{\"question\":\"Which healthcare areas are commonly targeted by AI-powered wearables?\",\"answer\":\"They are used for monitoring chronic conditions and mental health, and they can assist with early diagnosis of acute respiratory or cardiovascular events through continuous tracking.\"},{\"question\":\"What major obstacles affect adoption of AI-driven wearable health technology?\",\"answer\":\"Data privacy, connectivity with existing healthcare systems, regulatory validation, and ensuring robust model generalization and accessibility remain key issues.\"}]","AI and machine learning applications in wearable health devices - 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