Over the last few decades, cardiovascular diseases are the most common cause of death worldwide. It is the unpredictability and random time of the occurrence that makes the disease more dangerous. The mortality rate can be reduced by continuous supervision of clinicians and early detection of cardiac diseases. Unfortunately, people suffering from sudden cardiac arrests have low survival rates.
In this era of increasingly personalized patient care brought on by the COVID-19 pandemic, the cardiovascular community must familiarize themselves with wearable technologies and their wide range of clinical applications to achieve medical breakthroughs. ECG patch recorders and vests, patches, and textiles with in-built sensors, etc. are such wearable devices targeted at the healthcare professions for the early detection of acute decompensation and improved prognostication.
An opportunity to provide an Internet of Things solution has become easy with the increase of popularity of smart wearable devices. We proposed the wearable device which is used for adaptive fall detection for paralyzed patients/elders and heart stroke prediction. Real-time data of the patient such as blood pressure, body temperature, heart rate, and humidity can be monitored and analyzed by the machine learning algorithm. Thing Speak cloud server is used to store the real-time data received from the sensors associated with the patient and visualize it. The machine-learning algorithm is used to analyze the data and to predict the possibility of cardiac arrests with high accuracy. Our proposed wearable device saves the lives of patients and reduces the death rate by taking immediate care.