Exploring Internet of Things and Artificial Intelligence for Smart Healthcare Solutions

Exploring Internet of Things and Artificial Intelligence for Smart Healthcare Solutions

G. Yamini
DOI: 10.4018/978-1-7998-3591-2.ch013
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Artificial intelligence integrated with the internet of things network could be used in the healthcare sector to improve patient care. The data obtained from the patient with the help of certain medical healthcare devices that include fitness trackers, mobile healthcare applications, and several wireless sensor networks integrated into the body of the patients promoted digital data that could be stored in the form of digital records. AI integrated with IoT could be able to predict diseases, monitor heartbeat rate, recommend preventive maintenance, measure temperature and body mass, and promote drug administration by having a review with the patient's medical history and detecting health defects. This chapter explores IoT and artificial intelligence for smart healthcare solutions.
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In certain areas machines could be used for a certain process such as decision making, solving problems, learning and face recognition and this is possible with the help of artificial intelligence. The process of learning and making self-decisions could be done with the help of AI with the organized and unorganized data. AI is found to be ruling this digital world similar to mobile phones. Evolution in technology makes human lives easier and bringing out a new revolution in today world. Example of this advanced technology includes the Internet of Things (IoT) as shown in figure 1. Users experience is boosted up with the role of AI. The basic question regarding the IoT arises is that, “Why IoT cannot work without AI?” and “where and when IoT is required?” IoT is the technology that is composed of the combination of several sensors and embedding networks, which helps in data communication and interaction without the need for human intervention. The data gathered could be of real-time and these data could be stored in the cloud for further analysis.

The huge amount of data gathered with the help of IoT is possible from the varying environment. The collective data are converted into an application with the help of AI by the application of data analytics to make certain decisions. Therefore, the entire process is based on the collection and processing of data that needs an innovative way of implementing AI.

Figure 1.

Popularity of Internet of Things


The tenability of big data had been concluded recently, which seems to be very large and incomparable. This could be unlocked with the combination of IoT with AI. According to Deloitte, the combination of AI and IoT claims to hit the market in the near future since it had already been through implementation in several markets. Several companies use the combination of AI-based IoT heads up the competitive industries with their speedy data analysis and intelligent reasoning. IoT devices are capable of transmitting millions of data that may be or may not be required for certain aspects of analysis as shown in figure 2. AI technology enhances this process by managing the IoT devices with its ultimate processing and learning abilities that are made possible with the powerful subset known as Machine learning .

IoT is said to be known as the data “supplier” and machine learning is called as data “miner”. The main aim of machine learning is to predict data (Udendhran, 2017). The first process involved in machine learning is to refine the data supplied by the IoT. Numerous data arises from several sensors implemented under the IoT platform in varying environments. The correlation among the data is identified with the machine learning process and its main task is to mine the necessary data by having meaningful insights from those occurred variables and transit them for further analysis (Sharma et al. 2018). The traditional way of data analytics includes the gathering of past data, report generation and finally produce a result with data processing. Prediction is the main process that arrives with the desired outcome and searches interactions among the input variable in order to meet specific criteria (Amini, N et al 2011).

Figure 2.

Applications of Internet of Things


Once the data is transformed into actionable and valuable information, then big data comes into the part. This is not possible with the paper and pen records maintained manually.

AI plays a main role at this stage where the data is collected, and the meaningful extracts are obtained by data analytics. The similar patterns are revealed by AI once the data is fed and produces more informed decisions that could be done either with the help of humans or machines. Recently, business organization are trying to collaborate IoT with AI that tends to decrease the cost with the better user experience and also opens up a new pathway to these organizations(Chopra and White, 2011). In this process, the observer will promote the specific input data regarding the trigger element leading to the AEMs on the daily basis. This information will be reviewed by the doctors at a particular period of time.

Key Terms in this Chapter

Rational Decision Making: Rational decision making is said to be a crucial process unlike several other sectors, which offers certain provisions to the community.

Ubiquitous Computing (Ubicomp): Ubiquitous computing (UbiComp) is characterized by the use of small, networked and portable computer products in the form of smart phones, personal digital assistants and embedded computers built into many devices, resulting in a world in which each person owns and uses many computers.

Sensor-Cloud Infrastructure: Sensor-cloud infrastructure could be a technology that integrates cloud techniques into the WSNs. It provides users a virtual platform for utilizing the physical sensors in a clear and convenient approach.

Intelligent Information System: An intelligent information system is said to be the set of software and hardware that involves the skilled people for the process of decision making and co-ordination among the organization.

Data Supplier and Data Miner: Internet of things is said to be producer of data while machine learning is said to be data miner since it processes and analyzes the data produced from internet of things.

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