Edge Intelligence and Cloud Computing Health Monitoring System

Authors

  • O Nagamani, BTP Madhav, N Govind Rao, Govardhan Mattela, Raja Sekhar P V

Abstract

Now-a day’s most individuals are suffering from heart failure caused due to coronary artery disease. Coronary artery disease occurred because of the buildup of fatty deposits within the arteries. It will be expected to increase because of the present social lifestyle. Individuals who experience the ill effects of coronary artery disease will have many hazard factors like hypertension and weight have been expanding. Heart failure and blood pressure prediction in the population will help us to reduce coronary artery disease (cardiovascular mortality).
In this paper, the main motive is to update the “heart rate and blood pressure” results to the cloud and mobile phonewith the assistance of BLE and WI-FI for at regular intervals (5 minutes).
For data acquisition, we used the photoplethysmography sensor (MAX30105). By processing the PPG signals, the heart rate and blood pressure parameters are obtained. These parameters are given to a machine learning algorithm for getting accurate blood pressure and heart rate values.
The MAX30105 sensor module contains the twin - LED with 660nm (Red LED) and 905 nm (Infrared LED) wavelengths, with a sampling rate of 200 Hz. Right now, we are contemplating the PPG signal filtering, highlight extraction, and demonstrating. Espial of heart rate, systolic blood pressure and diastolic blood pressure with the root mean squared error (RMSE) of 7.90 beats every moment, 10.95 mmHg and 8.90 mmHg among Sphygmomanometer and PPG from five-crease cross-approval technique.

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Published

2020-05-10

Issue

Section

Articles