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This study describes the creation and evaluation of a low-cost internet of things (IoT)-based health monitoring system for the continuous monitoring of vital signs such as temperature, pulse rate, oxygen saturation (SpO 2 ) and blood pressure (BP) (both systolic and diastolic). Along with an organic light-emitting diode (OLED) display and an ESP8266 microcontroller, the system includes BP, non-contact temperature, SpO 2 and electrocardiogram (ECG) sensors. Using the visual programming tool, Node-RED, the data from these sensors are gathered, processed and transmitted to the Google Cloud platform for archival and visualisation. The process involved mounting the sensors and microcontrollers on a special printed circuit board and designing the circuit with EasyEDA. The device measures systolic, diastolic and pulse rates from the BP sensor, as well as temperature, ECG and SpO 2 values. The system works by using three push switches to read and display these values on demand. The gathered data are simultaneously shown on the OLED and sent to the Node-RED dashboard, where it is then sent to a Google Spreadsheet for archiving and analysis. This research article gives a thorough overview of the health monitoring system, the way it was implemented, and how it was successfully validated in a real-time setting. This study examines certain vital signs but additional health measures, such as respiration rate or glucose monitoring, could be included. Machine learning algorithms could also be used for predictive analytics. This would uncover data anomalies and trends early, improving healthcare management.
Mishra et al. (Thu,) studied this question.