Framework for Remotely Monitoring Patients Using Embedded Devices

Introduction

With the advancement in computing technology, it is now possible for human beings to interact and capture data from different aspects of life. One such enabling technology is the Internet of Things (IoT). IoT can be defined as the interconnection of devices, machines, and objects to share data using a computer network (Yu et al., 2021). IoT applications have now become popular in daily lives, more especially in healthcare such as monitoring activities and vital signs such as body temperature, blood pressure, and heart rate (Bisht et al., 2021). These developments have enabled the timely acquisition of information about health parameters using devices that can be worn, referred to as wearable devices. Wearable devices are devices that can collect data from a subject while being worn. These devices will typically have sensors to collect data, computer circuitry to process the data and network capability to transmit the data (Bhawiyuga et al., 2019). Some wearable devices go further with the ability to not only record vital signs but also perform data analysis and inform the user if an anomaly arises such as low blood pressure or low heartbeat rate (Bhat et al., 2020). Devices like smartwatches can even be configured to be able to independently summon medical assistance when it senses a lack of consciousness from the user (Edward C. Baig, 2020). Some applications and research have taken IoT technology further by having the devices transmit data to remote devices such as cloud servers for further analysis by healthcare practitioners (Tamhankar et al., 2020).

However, despite these advancements, there is still a lack of applications of these technologies in resource-constrained environments such as rural areas. This chapter, therefore, looks at developing a framework for remotely monitoring patients using embedded devices. The proposed framework looks at deploying a monitoring station in rural areas. This monitoring station consists of components that work together to measure patient vital signs, process these measurements as data and transmit the data for further analysis by healthcare professionals. The monitoring station can measure vital signs such as heartbeat rate, blood pressure, body temperature and respiration rate. This data along with environmental parameters such as wind speed, humidity and temperature is then transmitted to the cloud.

Therefore, this approach can increase access to healthcare in rural areas, especially in developing countries by allowing people within a community to share a monitoring station and healthcare professionals to perform diagnoses based on the data from the monitoring station. In developing countries, there is a high ratio of patients to healthcare professionals or facilities, hence introducing systems such as monitoring stations will assist in reducing this ratio by enabling healthcare professionals to perform diagnosis remotely based on vital signs captured by the monitoring station.

Therefore, deploying a monitoring station in a rural area will make it possible to also collect data about the health of the people within that community. This data can be analysed and used for decision making such as medical supplies to send to the community, uncovering an outbreak that might be affecting the community and even determining which healthcare professions are better suited for the community based on the health status of the people. All this can be derived from the vital signs of patients measured using a monitoring station.

The chapter starts by looking at related work and bases the framework design on past advancements in technology that can be applied in healthcare. The chapter then looks at the requirements required to implement the framework before outlining the conceptual model of the system and concluding with future work of the study.