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Home Projects Embedded A Lightweight and Secure Authentication Scheme for Remote Monitoring of Patients in IoMT
Prediction of Diabetes Using Machine Learning
Prediction of Diabetes Using Machine Learning Algorithms in Healthcare ₹2,000.00
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Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution
Agri-4-All: A Framework for Blockchain Based Agricultural Food Supply Chains in the Era of Fourth Industrial Revolution ₹5,500.00

A Lightweight and Secure Authentication Scheme for Remote Monitoring of Patients in IoMT

₹7,500.00

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Categories: IoT, Biomedical, Embedded, Internet of Things(IoT), Projects, Social Cause Tags: Arduino Nano, Biomedical, cloud, ECG, ECG Sensor, heartrate, IoMT, iot, LM35, Max30102
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Description

Aim:

Ā Ā Ā Ā Ā Ā Ā Ā  The mainstay of the project is to design and develop a system that monitors patient vital information and updates data’s to a authenticate cloud.

Introduction:

Ā  Ā  Ā Ā Ā Ā Ā  The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous and remote monitoring of patients through interconnected devices. This paper proposes a secure and efficient authentication scheme for remote patient monitoring using an Arduino Nano as the core controller. The system integrates multiple sensors, including the LM35 for body temperature, the Max30102 for heart rate and SPO2 levels, and an ECG sensor for heart electrical activity. An LCD provides real-time visual updates, while an ESP8266 module ensures seamless data transmission to the cloud. The system also includes an alert mechanism that triggers when abnormal sensor values are detected, displaying warnings on the LCD and sending alerts to the cloud. Ensuring data security and integrity is paramount in healthcare applications; thus, our authentication scheme is designed to protect sensitive patient information from unauthorized access. By leveraging lightweight cryptographic techniques, the system achieves a balance between robust security and resource efficiency, making it suitable for deployment in resource-constrained environments. This approach not only enhances the reliability and safety of patient monitoring systems but also contributes to the broader adoption of IoMT in healthcare, providing a scalable solution that can improve patient outcomes through timely and accurate health data collection and analysis.

Existing system:

Ā Ā  Ā Ā  Ā Ā  Existing remote patient monitoring systems often use microcontrollers and various sensors to track vital signs like body temperature, heart rate, SPO2 levels, and ECG data. These systems generally transmit data to healthcare providers via cloud services, but they frequently face challenges with data security and timely alerts. Many lack robust authentication mechanisms to prevent unauthorized access, and their alert systems may not always effectively highlight abnormal readings. Additionally, achieving efficiency in resource-constrained environments can be difficult, as complex security measures may impact system performance. These limitations underscore the need for more secure and responsive monitoring solutions.

Proposed system:

Ā Ā Ā Ā Ā Ā Ā  Our proposed system enhances existing remote patient monitoring solutions by using an Arduino Nano as the central controller, integrating various sensors including the LM35 for body temperature, the Max30102 for heart rate and SPO2 levels, and an ECG sensor for heart electrical activity. Data is displayed in real-time on an LCD, and transmitted to the cloud via an ESP8266 module. The system features an alert mechanism that promptly detects abnormal sensor readings, issuing notifications on the LCD and sending updates to the cloud to ensure timely intervention. The core focus of the system is a robust authentication scheme designed to secure patient data from unauthorized access, rather than employing encryption. This method ensures that sensitive information remains protected while maintaining system efficiency and ease of use. By integrating effective authentication with real-time monitoring and alerts, the system not only enhances patient care and safety but also supports broader adoption of IoMT technologies in healthcare. It offers a scalable and reliable solution that improves patient outcomes through accurate health data collection and prompt response to critical changes, addressing key needs in modern healthcare environments

Additional information
Weight 0.5 kg
Dimensions 25 × 20 × 10 cm
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