Heart Rate Monitoring Using IoT and AI for Aged Person: A Survey

Abstract

Today the internet of things (IoT) becomes limestone in the world, where lots of daily used objects are interconnected, and they are interacting with the environment for collecting information and automatically perform a specific task. The design and development of wearable systems for health monitoring has garnered lots of attention in the scientific community and in industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature connected devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient’s health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental, and activity status monitoring. Heart disease becomes critical fatality in the world today. Prediction of cardiovascular heart disease (CHD)—the most 40significant and critical challenge in the area of medical health care analysis. In this chapter, authors discussed how machine learning (ML) is used for effective in assisting in the making of a decision and predict. The patient health data are collected from a wearable device and how the data storing in blockchain for feature operations. ML now a day are used in various areas of the IoT. Multiple studies give a glimpse into a prediction of heart-related disease with the help of ML techniques. Authors also proposed few methods in this chapter for the automated health monitoring system (HMS) including quantifying patient’s heartbeat rate values, different ways for collecting the heartbeat rate of the patient, future prediction of heartbeat rates counting and a proposed ML method for heart disease classification.