Abstract

Keywords

۱٫ Introduction

۲٫ Sensors

۳٫ Wireless Communication Technologies

۴٫ Cloud Computing

۵٫ Machine Learning Techniques

۶٫ Fog/Edge Computing

۷٫ Applications

۸٫ Conclusion

Declaration of Competing Interest

References

Vitae

Abstract

The goal of Internet of Medical Things (IoMT) and digital healthcare systems is to provide people with the ease of receiving quality healthcare at the comfort of their homes. Hence, the aim of IoMT is the ubiquitous deployment of home-based healthcare systems. Making such systems intelligent and efficient for timely prediction of critical diseases can save millions of lives while simultaneously reducing the burden on the traditional healthcare systems e.g., hospitals. The advancement in IoT has enabled both patients and doctors to access real time data. This advancement has reduced the cost and energy consumption of digital healthcare systems by using efficient sensors and communication technologies. This paper provides a comprehensive review of various studies conducted for the development and improvement of IoMT. It analyses different sensors used for measurement of various parameters ranging from physiological to emotional signals. It also provides a detailed investigation of different communication technologies being used, their advantages, and limitations. Moreover, digital healthcare systems are now deploying machine learning technology for the prediction of health status of patients. These techniques and algorithms are also discussed. Data security and prediction accuracy are the main concerns in the development of this area. In conclusion, this paper reviews the various digital system designs in the context of healthcare, their methodology, limitations, and the present challenges faced by the e-health sector.

۱٫ Introduction

Quality healthcare is a basic human right, but one which fails to be provided adequately worldwide. The economic, environmental, and social development of this world and subsequent lifestyle changes have led to a drastic increase in chronic diseases such as heart disease, cancer, and diabetes. These chronic illnesses symbolize the greatest threat to human health. Moreover, each time an infectious disease breaks out, the hospitals are flooded with people which takes a huge toll on healthcare services. For example, currently there is a continuous stress on the worlds healthcare resources with the rampant spread of COVID-19. This kind of situation leads to inefficiency in managing patients and their data.

Experts believe that digital healthcare systems in Internet of Things (IoT) environments seem to be a compelling solution to this major healthcare problem. The building blocks and general architecture of a system in the IoT environment is shown in Fig. 1. In the context of medical services, the traditionally proposed remote health monitoring system architectures are divided into three layers: the vitals data collection layer from sensors; the transmission layer; and the analysis layer. The collection layer consists of sensors in the body area network (BAN). BAN collects the sensor data and transmits it to a gateway node. The transmission layer stores that data and analyzes it using conventional threshold values to report any abnormality. Additionally, the data may also be sent to the cloud for storage and heavy computations.