Key result
IoT-based myRIO system achieves ~92% heart rate accuracy, outperforming existing monitors.
Why the study?
With a growing and aging population, patient auto monitoring systems are needed to accurately acquire physiological parameters using smart sensors and IoT.
An IoT-based health monitoring system using myRIO demonstrated higher accuracy and processing speed for acquiring physiological parameters compared to existing microcontroller-based systems.
Answers are grounded in this paper and cite their sources.
May support IoT monitoring development; leaves open prospective validation before clinical use.
With the growing and aging population, patient auto monitoring systems are becoming more popular. Smart sensors linked with the internet of things (IoT) make patients' auto monitoring system possible. Nowadays myRIO with LabVIEW is more popular for easy data acquisition, instrument control, and automation. This paper proposed myRIO and IoT based health monitoring and diagnosing system (HMDS) to acquire heartbeat rate, pulse, blood pressure (BP), temperature and activities of the patient using various smart sensors with more accuracy. The acquired raw data from the various sensors had been sent to the myRIO using ESP 8266 Wi-Fi module. The received raw data by the myRIO would be processed to the equivalent medical parameters using LabVIEW and the same might be transferred to the remote monitoring system (RMS) using cloud via a gateway. The abnormalities in the obtained data would be monitored and the diagnosis was made. The experimental setup was developed using various wearable sensors, ESP 8266, myRIO with LabVIEW and cloud with the gateway.
No takes yet. Share an insight, caveat, or question.
Rathy et al. (2020) studied Health monitoring (n=24). IoT based health monitoring and diagnosing system (HMDS) using myRIO vs. SHCS-IoT and PHM was evaluated on Accuracy of heart rate monitoring. The proposed IoT-based health monitoring and diagnosing system using myRIO achieved 91.67% accuracy for heart rate and 95.83% for blood pressure, outperforming existing systems.
Synapse has enriched 10 related papers. Consider them for comparative context: