The proposed IoT-assisted ECG monitoring framework with Signal Strength Analysis achieved an average sensitivity of over 98% for ECG signal quality assessment and reduced energy consumption by 33%.
A novel IoT-assisted ECG monitoring framework with secure data transmission was developed and validated on standard databases, showing potential for continuous, reliable cardiovascular health monitoring.
The emerging Internet of Things(IoT) framework allows us to design small devices that are capable of sensing, processing and communicating, allowing sensors, embedding devices and other ' things ' to be created which will help to understand the surroundings. In this paper, the IoT assisted electrocardiogram (ECG) monitoring framework with secure data transmission has been proposed for continuous cardiovascular health monitoring. The development and implementation of a lightweight ECG Signal Strength Analysis has been proposed for automatic classification and realtime implementation, using ECG sensors, Arduino, Android phones, Bluetooth and cloud servers with the proposed IoT-assisted ECG monitoring system. For secure data transmission, the Lightweight Secure IoT (LS-IoT) and Lightweight Access Control (LAC) has been proposed. The ECG signals taken from the MIT-BIH and Physio Net Challenges databases and ECG signals for various physical activities are analyzed and checked in real-time. The proposed IoT assisted ECG monitoring framework has great potential to determine the clinical acceptance of ECG signals to improve the efficiency, accuracy and reliability of an unsupervised diagnostic system.
Guangyu Xu (Wed,) conducted a other in Cardiovascular health monitoring (n=20). IoT-assisted ECG monitoring framework with Signal Strength Analysis (SSA) vs. Existing SSA methods was evaluated on Sensitivity of Signal Strength Analysis (SSA) for ECG signal quality assessment. The proposed IoT-assisted ECG monitoring framework with Signal Strength Analysis achieved an average sensitivity of over 98% for ECG signal quality assessment and reduced energy consumption by 33%.