Context-Aware Remote Patient Monitoring System with IoT and Digital Security
Abstract
The “Context Awar Remote Patient Monitoring System with IoT and Digital Security” revolutionizes continuous healthcare by integrating IoT technology, sensors, and cloud computing to address the growing need for effective at-home patient monitoring. This system ensures vital health data is continuously recorded, analyzed, and made available to healthcare professionals in real-time. At its core is the Node MCU microcontroller, which connects key sensors to a cloud-based platform. The SPO2 sensor measures blood oxygen saturation to detect issues like hypoxemia, while the body temperature sensor tracks core temperature, signaling possible infections or fevers. An environmental temperature sensor monitors ambient conditions, crucial for patients with chronic illnesses or weakened immunity. Data is securely transmitted to the cloud for analysis using machine learning algorithms. These tools cross-reference vital and environmental data to detect health anomalies and predict early illness onset. For example, a drop in oxygen levels combined with a fever triggers alerts for potential respiratory infections, empowering healthcare providers to intervene promptly and prevent complications. This system's predictive capabilities and real-time alerts ensure timely responses to critical thresholds, enhancing patient safety and reducing hospital admissions. A user-friendly interface allows patients and caregivers to monitor health trends via mobile and web applications. By enabling continuous, remote monitoring, this solution benefits patients with chronic conditions, the elderly, and post-surgery individuals, providing peace of mind and reducing the workload on healthcare facilities. It promotes high-quality care from the comfort of patients' homes, aligning with modern telehealth needs. In conclusion, the “Context Awar Remote Patient Monitoring System with IoT and Digital Security” is a transformative tool for remote care, combining advanced sensor technology, real-time analytics, and machine learning to deliver improved outcomes, resource efficiency, and patient-centered healthcare.
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Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
Cites a retracted work: 10.1155/2021/2192913
MajorThe cited work was retracted on 2023-07-12 (notice 10.1155/2023/9809127), before this paper was published (2024-12-27). Its authors could have known.
Cited work 10.1155/2021/2192913 · retracted 2023-07-12 · notice 10.1155/2023/9809127 · this paper published 2024-12-27
“10.1155/2021/2192913”
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Research works printed but not linked: 1
- 10.1109/cspa48992.2020.9068699 · DOI
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- Authors
- Geetha, S., Karthikeyan, P., N, Y., D, P., & V, V.
- Affiliations
- Pondicherry University (IN)
- Sri Manakula Vinayagar Engineering College,Department of Computer Science and Engineering,Puducherry,India
- ORCID
- Geetha, S.
- Venue
- 2024 International Conference on System, Computation, Automation and Networking (ICSCAN)
- Publisher
- IEEE
- Published
- 2024-12-27
- Pages
- 1-6
- OpenAlex
- W4407950985
- Semantic Scholar
- 193c51d5907e181f2b42af615a7c80108df1ae4a
- Cited by
- 1
- References
- 17
- Citations by year
- 2025: 1
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on System, Computation, Automation and Networking (ICSCAN) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Keywords
- remote patient monitoring, Internet of Things, NodeMCU, SpO2 sensor, body temperature sensor, cloud-based health data, machine learning anomaly detection, telemedicine, digital security, real-time alerts, chronic disease management, home health monitoring
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