{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:06:53Z","timestamp":1772449613614,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob\/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan\u2013Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 \u00b1 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems.<\/jats:p>","DOI":"10.3390\/fi18030130","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T10:24:34Z","timestamp":1772447074000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Edge\u2013Fog\u2013Cloud IoT Framework for Real-Time Cardiac Monitoring and Rapid Clinical Alerts in Hospital Wards"],"prefix":"10.3390","volume":"18","author":[{"given":"Tehseen","family":"Baig","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Punjab, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4252-8375","authenticated-orcid":false,"given":"Nauman Riaz","family":"Chaudhry","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Punjab, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-0909","authenticated-orcid":false,"given":"Reema","family":"Choudhary","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Gujrat, Gujrat 50700, Punjab, Pakistan"}]},{"given":"Pankaj","family":"Yadav","sequence":"additional","affiliation":[{"name":"Tech Mahindra Limited, Bangalore 560100, Karnataka, India"}]},{"given":"Younus Ahamad","family":"Shaik","sequence":"additional","affiliation":[{"name":"American Megatrends Inc., Duluth, GA 30097, USA"}]},{"given":"Ayesha","family":"Rashid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Punjab, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E.H., Alzoubi, K., Khandakar, A., Khallifa, R., Abouhasera, R., Koubaa, S., Ahmed, R., and Hasan, M.A. (2019). Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents. Sensors, 19.","DOI":"10.3390\/s19122780"},{"key":"ref_2","unstructured":"(2026, February 22). Cardiovascular Diseases (CVDs). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3272","DOI":"10.1093\/cvr\/cvac013","article-title":"Global burden of heart failure: A comprehensive and updated review of epidemiology","volume":"118","author":"Savarese","year":"2023","journal-title":"Cardiovasc. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1164\/rccm.201801-0088CI","article-title":"Arterial Pulse Pressure Variation with Mechanical Ventilation","volume":"199","author":"Teboul","year":"2019","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e033306","DOI":"10.1161\/JAHA.123.033306","article-title":"Association Between ECG Abnormalities and Mortality in a Low-Risk Population","volume":"13","author":"Lee","year":"2024","journal-title":"J. Am. Heart Assoc. Cardiovasc. Cerebrovasc. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e18750362295563","DOI":"10.2174\/0118750362295563240620111209","article-title":"The Clinical Relevance of ECG Parameters in the Prediction of Cardiac Mortality: A Comprehensive Review","volume":"17","author":"Singh","year":"2024","journal-title":"Open Bioinforma. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.11591\/eei.v14i5.9599","article-title":"An internet of things-enabled wearable device for stress monitoring and control","volume":"14","author":"Tyulepberdinova","year":"2025","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17994","DOI":"10.1038\/s41598-025-99838-4","article-title":"IoT-enabled real-time health monitoring system for adolescent physical rehabilitation","volume":"15","author":"Yang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Orro, A., Geminiani, G.A., Sicurello, F., Modica, M., Pegreffi, F., Neri, L., Augello, A., and Botteghi, M. (2024). A Cloud Infrastructure for Health Monitoring in Emergency Response Scenarios. Sensors, 24.","DOI":"10.20944\/preprints202409.0440.v1"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alenoghena, C.O., Onumanyi, A.J., Ohize, H.O., Adejo, A.O., Oligbi, M., Ali, S.I., and Okoh, S.A. (2022). eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions. Int. J. Environ. Res. Public. Health, 19.","DOI":"10.3390\/ijerph192013071"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Iqbal, A., Nauman, A., Qadri, Y.A., and Kim, S.W. (2025). Optimizing Spectral Utilization in Healthcare Internet of Things. Sensors, 25.","DOI":"10.3390\/s25030615"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10462-025-11342-3","article-title":"A comprehensive review on key technologies toward smart healthcare systems based IoT: Technical aspects, challenges and future directions","volume":"58","author":"Alsabah","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Writing Committee Members, Greenland, P., Alpert, J.S., Beller, G.A., Benjamin, E.J., Budoff, M.J., Fayad, Z.A., Foster, E., Hlatky, M.A., and Hodgson, J.M. (2010). 2010 ACCF\/AHA Guideline for Assessment of Cardiovascular Risk in Asymptomatic Adults: A Report of the American College of Cardiology Foundation\/American Heart Association Task Force on Practice Guidelines. Circulation, 122, 25.","DOI":"10.1161\/CIR.0b013e3182051b4c"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.bpa.2019.04.002","article-title":"Perioperative hemodynamic management 4.0","volume":"33","author":"Michard","year":"2019","journal-title":"Best Pract. Res. Clin. Anaesthesiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ijnurstu.2018.04.013","article-title":"The impact of continuous versus intermittent vital signs monitoring in hospitals: A systematic review and narrative synthesis","volume":"84","author":"Downey","year":"2018","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1007\/s00134-017-4755-7","article-title":"Acute kidney injury in sepsis","volume":"43","author":"Bellomo","year":"2017","journal-title":"Intensive Care Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Matsuzaka, Y., and Iyoda, M. (2025). Applications, image analysis, and interpretation of computer vision in medical imaging. Front. Radiol., 5.","DOI":"10.3389\/fradi.2025.1733003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gabriel, P., Rehani, P., Troy, T., Wyatt, T., Choma, M., and Singh, N. (2025). Continuous patient monitoring with AI: Real-time analysis of video in hospital care settings. Front. Imaging, 4.","DOI":"10.3389\/fimag.2025.1547166"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109531","DOI":"10.1016\/j.compbiomed.2024.109531","article-title":"Computer vision algorithms in healthcare: Recent advancements and future challenges","volume":"185","author":"Kabir","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ccc.2017.12.001","article-title":"The Afferent Limb of Rapid Response Systems: Continuous Monitoring on General Care Units","volume":"34","author":"Taenzer","year":"2018","journal-title":"Crit. Care Clin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1097\/EJA.0000000000000798","article-title":"Improving detection of patient deterioration in the general hospital ward environment","volume":"35","author":"Vincent","year":"2018","journal-title":"Eur. J. Anaesthesiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Amin, P., Anikireddypally, N.R., Khurana, S., Vadakkemadathil, S., and Wu, W. (2019). Personalized Health Monitoring using Predictive Analytics. Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), IEEE.","DOI":"10.1109\/BigDataService.2019.00048"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rjeily, C.B., Badr, G., Hassani, A.H.A., and Andres, E. (2017). Predicting heart failure class using a sequence prediction algorithm. Proceedings of the 2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME), IEEE.","DOI":"10.1109\/ICABME.2017.8167546"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"854","DOI":"10.4236\/wjet.2018.64057","article-title":"Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System","volume":"6","author":"Nashif","year":"2018","journal-title":"World J. Eng. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100222","DOI":"10.1016\/j.iot.2020.100222","article-title":"Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing","volume":"11","author":"Tuli","year":"2020","journal-title":"Internet Things"},{"key":"ref_26","first-page":"8274","article-title":"To Predict Heart Disease Risk and Medications Using Data Mining Techniques with an IoT Based Monitoring System for Post Operative Heart Disease Patients","volume":"4","author":"B","year":"2017","journal-title":"Int. J. Emerg. Trends Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Almujally, N.A., Aljrees, T., Saidani, O., Umer, M., Faheem, Z.B., Abuzinadah, N., Alnowaiser, K., and Ashraf, I. (2023). Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System. Sensors, 23.","DOI":"10.3390\/s23104580"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ed-Daoudy, A., and Maalmi, K. (2019). Real-time machine learning for early detection of heart disease using big data approach. Proceedings of the 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), IEEE.","DOI":"10.1109\/WITS.2019.8723839"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e646","DOI":"10.7717\/peerj-cs.646","article-title":"A new smart healthcare framework for real-time heart disease detection based on deep and machine learning","volume":"7","author":"Elwahsh","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1186\/s40537-019-0244-x","article-title":"Analysis and classification of heart diseases using heartbeat features and machine learning algorithms","volume":"6","author":"Alarsan","year":"2019","journal-title":"J. Big Data"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kappiarukudil, K.J., and Ramesh, M.V. (2010). Real-Time Monitoring and Detection of \u201cHeart Attack\u201d Using Wireless Sensor Networks. Proceedings of the 2010 Fourth International Conference on Sensor Technologies and Applications, IEEE.","DOI":"10.1109\/SENSORCOMM.2010.99"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18738","DOI":"10.1038\/s41598-021-97118-5","article-title":"ECG-based machine-learning algorithms for heartbeat classification","volume":"11","author":"Aziz","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s13246-020-00863-6","article-title":"Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques","volume":"43","author":"Subasi","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"S, V.P.D., Jayanthy, S., and Thangarajan, K. (2025). Hybrid Ensemble and Supervised Algorithm Based Heart Disease Monitoring System Using IoT Sensor. Proceedings of the 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE.","DOI":"10.1109\/ICIRCA65293.2025.11089585"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6611366","DOI":"10.1155\/2021\/6611366","article-title":"The Internet of Things in Geriatric Healthcare","volume":"2021","author":"Sahu","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Manogaran, G., Shakeel, P., Fouad, H., Nam, Y., Baskar, S., Chilamkurti, N., and Sundarasekar, R. (2019). Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System. Sensors, 19.","DOI":"10.3390\/s19133030"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"32258","DOI":"10.1109\/ACCESS.2018.2846609","article-title":"UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities","volume":"6","author":"Muhammed","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.future.2017.02.014","article-title":"Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach","volume":"78","author":"Rahmani","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sreejith, S., Rahul, S., and Jisha, R.C. (2016). A Real Time Patient Monitoring System for Heart Disease Prediction Using Random Forest Algorithm. Advances in Signal Processing and Intelligent Recognition Systems, Springer.","DOI":"10.1007\/978-3-319-28658-7_41"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shah, A., Ahirrao, S., Pandya, S., Kotecha, K., and Rathod, S. (2021). Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk. Front. Public Health, 9.","DOI":"10.3389\/fpubh.2021.762303"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/JSEN.2019.2942099","article-title":"An integrated wearable wireless vital signs biosensor for continuous inpatient monitoring","volume":"20","author":"Wong","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37349","DOI":"10.1109\/ACCESS.2024.3373646","article-title":"Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach","volume":"12","author":"Naeem","year":"2024","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.58414\/SCIENTIFICTEMPER.2024.15.2.37","article-title":"Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems","volume":"15","author":"Gold","year":"2024","journal-title":"Sci. Temper"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nancy, A.A., Ravindran, D., Vincent, P.M.D.R., Srinivasan, K., and Reina, D.G. (2022). IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning. Electronics, 11.","DOI":"10.3390\/electronics11152292"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khan, N., and Imtiaz, M.N. (2022). Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals. arXiv.","DOI":"10.32920\/22734308"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pati, A., Parhi, M., Alnabhan, M., Pattanayak, B.K., Habboush, A.K., and Al Nawayseh, M.K. (2023). An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis. Informatics, 10.","DOI":"10.3390\/informatics10010021"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Writing Committee Members, Jones, D.W., Ferdinand, K.C., Taler, S.J., Johnson, H.M., Shimbo, D., Abdalla, M., Altieri, M.M., Bansal, N., and Bello, N.A. (2025). 2025 AHA\/ACC\/AANP\/AAPA\/ABC\/ACCP\/ACPM\/AGS\/AMA\/ASPC\/NMA\/PCNA\/SGIM Guideline for the Prevention, Detection, Evaluation and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines. Hypertension, 82, e212\u2013e316.","DOI":"10.1161\/HYP.0000000000000249"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/3\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T10:36:46Z","timestamp":1772447806000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/3\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,2]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["fi18030130"],"URL":"https:\/\/doi.org\/10.3390\/fi18030130","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,2]]}}}