The recent advancements of the emerging technologies (i.e., internet of things [IoT] and machine learning [ML]) has rapidly transformed the healthcare industry in a better way. IoT-enabled wearable and monitoring devices are making new methods to manage patient's health. Sensor-enabled devices are useful for incessant data collection and remote patient monitoring. This enables the healthcare providers to impart more significant healthcare settings. Machine learning classifiers are useful in classifying different diseases and also rank the state of the disease. These two technologies potentially reduce the healthcare costs and accredit the patients to take control of their health condition and improvement. However, different challenges are associated with the implementation of these technologies in healthcare including security, data privacy and availability, proper system integration, and data transfer. This chapter aims to provide an overview of different approaches and security aspects of these emerging technologies in the advancement of remote healthcare monitoring.
Top1. Introduction
The healthcare industry has evolved since last decade, emerging technologies i.e., ML and IoT is one of the main reasons behind the Health Monitoring System transformation. IoT is the network of internet-enabled physical devices that includes sensors, small devices with software and internet connectivity. Being connected with other devices, these devices are capable of collecting and exchanging data amongst them. ML algorithms are capable of classifying the data based on different features. So, the Health Monitoring System using these two technologies is an innovative advancement to monitor the patients in real-time. (Selvaraj, S., et. al.) in their paper shows that the technology includes Wireless-Body-Area-Network (WBAN), wearable sensors and devices to gather data from numerous health indicator i.e., patients’ blood pressure and heart rate, blood sugar and oxygen level etc. Whereas, (Ukil, A., et. al.) in their work shows that after collecting the relevant health data, ML algorithms then analyze the data, interpret and provide useful information regarding patients’ health. Doctors and medical practitioners monitor the live status of the patients remotely through the internet without visiting in-person. This helps to reduce medical errors and increase the efficiency of healthcare treatment delivery and helps in improving patients’ health condition. Mishra, S., et. al. shows that the technology-based Healthcare System helps the medical practitioners and doctors to identify potential risks and the severity of any chronic disease. On the other hand, Durga et. al. manifest that it can help to avert the development of different chronic diseases, reduce hospitalizations and improves the overall health condition of the patient. Engaging these technologies, health practitioners can get a deeper knowledge about a patient's day to day health status. Figure 1 demonstrates the IOT and ML based monitoring system.
Figure 1. Applications of IoT in healthcare sector
The chapter is formatted as follows: the next section describes the benefit of continuous health monitoring. Section 3 continues with the accessibility and convenience of the WBAN sensors, whereas section 4 elaborates how IOT and ML can improve healthcare management further and what is the potential for the same. Section 5 illustrates the future of health monitoring with the emerging technologies, and section 6 concludes the chapter.
Top2. Benefits For Continuous Health Assessment
Continuous health assessment has emerged as an innovative initiative for tracking the patient’s health status in real-time. IOT sensors i.e., WBAN and other wearable sensors are used to collect data securely and constantly. Onasanya, A., et. al. exhibit that different types of data like blood sugar level, blood pressure level, blood oxygen level, heart rate are collected. These collected are then transmitted through IoT technology to the cloud storage server. Here, using different ML algorithms these data are analyzed for providing accurate diagnosis and time-to-time intervention when needed. Lee, B. M. et. al. shows that continuous health monitoring also provides accessibility and convenience to patients, where they can track and act upon real-time health monitoring situations. Collected data then sent to the doctors for further treatment. This saves time and hospitalization along with more accurate decisions. By continuously monitoring the health status, different fatal diseases can be detected early. This saves several lives from fatal chronic illness. Qahtan, S., et. al. demonstrates that different multinational companies like Apple, Google have developed smart watches, which are capable of gathering heart rate, blood pressure and oxygen level, ECG diagrams. Different companies in the healthcare industry are also adopting these IoT based wearable sensors to remotely collect medical data. Few of these companies along with their adopted IoT schemes are discussed below:
Philips Healthcare - “Smart care pathways”, an innovative IoT scheme that involves AI and ML to analyze patients’ medical data to develop personalized medical care.
Siemens Healthineers - “Teamplay”, an IoT based cloud platform, that gives medical practitioners and doctors to collaborate and share medical data for research and treatment purposes.
IBM Watson Health - “Watson Health Cloud” is another IoT based technique, that is capable of collecting and analyzing patients’ data and find insights from it.