Delay Analysis of ECG Monitoring in Resource Constrained Devices

The Internet of Things (IoT) is revolutionizing healthcare by integrating smart devices and sensors into medical practice. These devices can monitor patient health remotely, collect real-time data, and enable healthcare providers to deliver more personalized and timely care. From wearable fitness trackers that monitor vital signs to smart pills that track medication adherence, IoT in healthcare enhances patient outcomes, improves healthcare delivery efficiency, and reduces costs. An electrocardiogram (ECG) is an essential diagnostic tool for determining heart -related problems. In this research, we have developed a real -time ECG data acquisition method using small devices like Raspberry Pi Pico (RPiPico) and AD8232 ECG sensors. Our study analyses the ECG signal transmission on small embedded devices and a standalone machine so that when such critical data is transmitted on cloud resources via edge devices, the delay taken to transmit the data impacts the significant decisions of embedding the algorithms in resource-constrained devices. We analyzed the delays while the data was transmitted through the MQTT broker. We have also compared CPU and memory usage running on small and standalone devices. We observe that when the broker runs on the Rpi, it gives less delay, CPU utilization, and memory usage compared to the laptop as a broker. There is no packet loss in both cases. Thus, transferring critical data via a small processor like RPi with an MQTT broker is faster than transferring it through a general laptop processor with an MQTT broker.