{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T12:59:27Z","timestamp":1785157167139,"version":"3.55.0"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":207,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Telemedicine and Applications"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Mobile health has become a popular option for patients to monitor and analyze their body activities and vital signs using their mobile devices, such as smartphones and smartwatches. In addition, the healthcare community has begun using artificial intelligence (AI) models to automate the diagnosis of abnormal conditions and diseases, particularly in real\u2010time emergency scenarios. This article introduces a new deep learning model to automatically identify daily body activities. We propose a hybrid AI model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU) for robust human activity classification. Key contributions include (1) the design of an end\u2010to\u2010end CNN\u2010GRU model that jointly extracts local spatiotemporal features and models long\u2010term temporal dependencies from raw sensor data, and (2) a comprehensive benchmarking framework validating the developed model against previous machine learning and deep learning classifiers. A public mobile health dataset (MHEALTH) has been used in this study. This dataset includes 12 physical activities, for example, knee bending, walking, and running. Key findings demonstrate that the CNN\u2010GRU model achieves a state\u2010of\u2010the\u2010art classification accuracy of 99.50% on the publicly available MHEALTH dataset, which encompasses 12 distinct physical activities. It significantly outperforms CNN\u2010LSTM (98.83%), 1\u2010D CNN (96.89%), and traditional ensemble methods while maintaining high precision, recall, and F1\u2010scores across all activity classes. Therefore, our developed model can be implemented in a cloud computing system to monitor senior patients as a critical healthcare application.<\/jats:p>","DOI":"10.1155\/ijta\/1465162","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T12:02:28Z","timestamp":1785153748000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Convolutional\u2010Gated Recurrent Neural Network for Robust Mobile Health Activities 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